{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8d6260f",
   "metadata": {},
   "source": [
    "## 前期配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d86858e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关包\n",
    "import os\n",
    "import re\n",
    "import cv2\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# 导入预训练CNN\n",
    "from tensorflow.keras.applications import efficientnet\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "\n",
    "import json\n",
    "import jieba\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6625228c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
    "\n",
    "if gpus:\n",
    "    gpu0 = gpus[0]                                        #如果有多个GPU，仅使用第0个GPU\n",
    "    tf.config.experimental.set_memory_growth(gpu0, True)  #设置GPU显存用量按需使用\n",
    "    tf.config.set_visible_devices([gpu0],\"GPU\")\n",
    "    \n",
    "gpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "893d7bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置基本参数\n",
    "\n",
    "# 图片地址\n",
    "IMAGES_PATH = \"./ai_challenger_caption_validation_20170910/caption_validation_images_20170910/\"\n",
    "\n",
    "# 目标大小\n",
    "IMAGE_SIZE = (299, 299)\n",
    "\n",
    "# 词汇量大小\n",
    "VOCAB_SIZE = 10000\n",
    "\n",
    "# 输出句子单词长度\n",
    "SEQ_LENGTH = 25\n",
    "\n",
    "# 特征向量长度\n",
    "EMBED_DIM = 512\n",
    "\n",
    "# 输出层维度大小\n",
    "FF_DIM = 512\n",
    "\n",
    "# 训练参数\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 30\n",
    "AUTOTUNE = tf.data.AUTOTUNE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6858e2a",
   "metadata": {},
   "source": [
    "### 将图片和它的“label”对应起来"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "816c94a9",
   "metadata": {},
   "source": [
    "这一步我们把图片相对路径和其5个caption以字典的形式对应起来，过程中要使用jieba进行中文分割，加上start和end，并且去掉带有不合格caption的例子，保证我们的输入在其中一个维度大小是5。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b000f620",
   "metadata": {},
   "outputs": [],
   "source": [
    "token_len = [] # 用于后面统计句子中词的长度\n",
    "\n",
    "def load_captions_json(filename):\n",
    "    \n",
    "    caption_mapping = {} # 映射字典  image1(path):[caption1,caption2...]\n",
    "    text_data = [] # 把合格的处理好的caption放到这里 后面用来向量化\n",
    "    images_to_skip = set() # 用来保存不合格的标注，后面在caption_mapping去掉对应的图片及其注释\n",
    "    \n",
    "    # 打开并读取json文件\n",
    "    with open(filename) as f:\n",
    "        json_data = json.load(f)\n",
    "        # 遍历3W个json数据\n",
    "        for item in tqdm.tqdm(json_data):\n",
    "            # 图片的名字“id”\n",
    "            img_name = item['image_id']\n",
    "            # 图片的路径 \n",
    "            img_path = os.path.join(IMAGES_PATH, img_name.strip())\n",
    "            # 遍历属于每个图片的5个标注\n",
    "            for caption in item['caption']:\n",
    "                # 分词\n",
    "                tokens =[word for word in jieba.cut(caption)]\n",
    "                # 根据tokens构造caption（空格分隔的字符串）\n",
    "                caption = \" \".join(tokens)   \n",
    "                \n",
    "                # 存入句子中词的长度\n",
    "                token_len.append(len(tokens))\n",
    "                \n",
    "                if len(tokens) < 3 or len(tokens) > SEQ_LENGTH:\n",
    "                    images_to_skip.add(img_path)\n",
    "                    continue\n",
    "\n",
    "                # 如果文件名以jpg结尾，且标注不在images_to_skip中\n",
    "                if img_path.endswith(\"jpg\") and img_path not in images_to_skip:\n",
    "                    # 增加开始和结束token\n",
    "                    caption = \"<start> \" + caption.strip() + \" <end>\"\n",
    "                    text_data.append(caption)\n",
    "\n",
    "                    if img_path in caption_mapping:\n",
    "                        # 追加\n",
    "                        caption_mapping[img_path].append(caption)\n",
    "                    else:\n",
    "                        # 初始化\n",
    "                        caption_mapping[img_path] = [caption]\n",
    "                        \n",
    "        # 如果文件名在images_to_skip中，则将caption_mapping中的元素删除掉\n",
    "        # 即这里可能有的caption不是5个\n",
    "        for img_path in images_to_skip:\n",
    "            if img_path in caption_mapping:\n",
    "                del caption_mapping[img_path]\n",
    "                \n",
    "        return caption_mapping, text_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37538265",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                        | 0/30000 [00:00<?, ?it/s]Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\suichu\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.369 seconds.\n",
      "Prefix dict has been built successfully.\n",
      "100%|██████████████████████████████████████████████████████████████████████████| 30000/30000 [00:10<00:00, 2816.87it/s]\n"
     ]
    }
   ],
   "source": [
    "# 加载数据\n",
    "captions_mapping, text_data = load_captions_json(\"./ai_challenger_caption_validation_20170910/caption_validation_annotations_20170910.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "748ce2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.015133333333333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可见句子中词的平均长度为13 符合我们上方所设置的参数 如果不符合可以进行微调\n",
    "np.array(token_len).mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20048f0c",
   "metadata": {},
   "source": [
    "此时返回的：\n",
    "\n",
    "captions_mapping则是一个字典，键为图片的相对路径，值是一个列表，里面是其5个caption。 text_data是一个列表，里面是全部的caption,和captions_mapping.values()结果应该是一样的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3d71459",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6a8c37e5",
   "metadata": {},
   "source": [
    "### 设置训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b918715",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size=0.8\n",
    "\n",
    "# all_images列表里是所有图片的文件路径\n",
    "all_images = list(captions_mapping.keys())\n",
    "# 打乱顺序\n",
    "np.random.shuffle(all_images)\n",
    "# 获取训练集数量\n",
    "train_size = int(len(captions_mapping) * train_size)\n",
    "\n",
    "train_data = {\n",
    "    img_name: captions_mapping[img_name] for img_name in all_images[:train_size]\n",
    "}\n",
    "valid_data = {\n",
    "    img_name: captions_mapping[img_name] for img_name in all_images[train_size:]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27ebfec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23896, 5975)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data),len(valid_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "205e42c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8c70425a",
   "metadata": {},
   "source": [
    "### 文本向量化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d6efe91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除句子中的特殊符号\n",
    "strip_chars = \"!\\\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\n",
    "strip_chars = strip_chars.replace(\"<\", \"\") # 因为我们句子中有\"<start>\" \"<end>\"\n",
    "strip_chars = strip_chars.replace(\">\", \"\")\n",
    "\n",
    "\n",
    "def custom_standardization(input_string):\n",
    "    # 全部转为小写\n",
    "    lowercase = tf.strings.lower(input_string)\n",
    "    return tf.strings.regex_replace(lowercase, \"[%s]\" % re.escape(strip_chars), \"\")\n",
    "\n",
    "\n",
    "vectorization = TextVectorization(\n",
    "    max_tokens=VOCAB_SIZE,  # 词汇量大小 最上方设置10000\n",
    "    output_mode=\"int\", \n",
    "    output_sequence_length=SEQ_LENGTH, # 输出句子长度 最上方设置25\n",
    "    standardize=custom_standardization,\n",
    ")\n",
    "\n",
    "vectorization.adapt(text_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d6f3fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '[UNK]',\n",
       " '的',\n",
       " '<start>',\n",
       " '<end>',\n",
       " '一个',\n",
       " '在',\n",
       " '上',\n",
       " '男人',\n",
       " '着',\n",
       " '穿着',\n",
       " '女人',\n",
       " '有',\n",
       " '两个',\n",
       " '站',\n",
       " '人',\n",
       " '里',\n",
       " '拿',\n",
       " '戴着',\n",
       " '右手',\n",
       " '双手',\n",
       " '球场上',\n",
       " '走',\n",
       " '左手',\n",
       " '道路',\n",
       " '和',\n",
       " '坐在',\n",
       " '运动场',\n",
       " '球衣',\n",
       " '三个',\n",
       " '旁边',\n",
       " '舞台',\n",
       " '上衣',\n",
       " '运动服',\n",
       " '房间',\n",
       " '一群',\n",
       " '旁',\n",
       " '帽子',\n",
       " '前面',\n",
       " '包',\n",
       " '踢足球',\n",
       " '黑色',\n",
       " '衣服',\n",
       " '前',\n",
       " '室内',\n",
       " '足球',\n",
       " '草地',\n",
       " '大厅',\n",
       " '运动员',\n",
       " '一位',\n",
       " '室外',\n",
       " '白色',\n",
       " '话筒',\n",
       " '裙子',\n",
       " '东西',\n",
       " '眼镜',\n",
       " '屋子里',\n",
       " '街道',\n",
       " '放在',\n",
       " '四个',\n",
       " '坐',\n",
       " '干净',\n",
       " '面带微笑',\n",
       " '深色',\n",
       " '足球场',\n",
       " '身穿',\n",
       " '平坦',\n",
       " '打',\n",
       " '孩子',\n",
       " '人旁',\n",
       " '墨镜',\n",
       " '抱',\n",
       " '抢',\n",
       " '打篮球',\n",
       " '球服',\n",
       " '明亮',\n",
       " '争抢',\n",
       " '插',\n",
       " '头发',\n",
       " '短袖',\n",
       " '抬起',\n",
       " '表演',\n",
       " '宽敞',\n",
       " '房屋',\n",
       " '手里',\n",
       " '裤子',\n",
       " '交谈',\n",
       " '桌子',\n",
       " '小孩',\n",
       " '男士',\n",
       " '衣着',\n",
       " '椅子',\n",
       " '广告牌',\n",
       " '女孩',\n",
       " '蹲',\n",
       " '房屋里',\n",
       " '背着',\n",
       " '外套',\n",
       " '不同',\n",
       " '红色',\n",
       " '西装',\n",
       " '短裤',\n",
       " '女士',\n",
       " '给',\n",
       " '各异',\n",
       " '旁有',\n",
       " '搂',\n",
       " '说话',\n",
       " '拎',\n",
       " '手机',\n",
       " '连衣裙',\n",
       " '看着',\n",
       " '绿茵茵',\n",
       " '讲话',\n",
       " '宽阔',\n",
       " '人前',\n",
       " 't台',\n",
       " '、',\n",
       " '唱歌',\n",
       " '沙发',\n",
       " '穿',\n",
       " '绿油油',\n",
       " '长发',\n",
       " '整洁',\n",
       " '碧绿',\n",
       " '浅色',\n",
       " '汽车',\n",
       " '走秀',\n",
       " '展板',\n",
       " '蓝色',\n",
       " '前有',\n",
       " '奔跑',\n",
       " '男孩',\n",
       " '身前',\n",
       " '口袋',\n",
       " '叉腰',\n",
       " '红毯',\n",
       " '兜',\n",
       " '球拍',\n",
       " '高尔夫球',\n",
       " '球杆',\n",
       " '行走',\n",
       " '牵着',\n",
       " '篮球',\n",
       " '人群',\n",
       " '挎着',\n",
       " '地站',\n",
       " '打网球',\n",
       " '篮球场',\n",
       " '腿',\n",
       " '扎',\n",
       " '身后',\n",
       " '地',\n",
       " '外',\n",
       " '工作',\n",
       " '做',\n",
       " '中间',\n",
       " '躺',\n",
       " '沙滩',\n",
       " '看',\n",
       " '跪',\n",
       " '披',\n",
       " '面带笑容',\n",
       " '草坪',\n",
       " '干活',\n",
       " '抓',\n",
       " '旁站',\n",
       " '海边',\n",
       " '手',\n",
       " '搭',\n",
       " '老人',\n",
       " '大',\n",
       " '灯光',\n",
       " '牛仔裤',\n",
       " '马路上',\n",
       " '房屋内',\n",
       " '摸',\n",
       " '抬着',\n",
       " '跳舞',\n",
       " '休闲',\n",
       " '双臂',\n",
       " '棚里',\n",
       " '长袖',\n",
       " '地上',\n",
       " '手套',\n",
       " '下围棋',\n",
       " '边',\n",
       " '扶',\n",
       " '右肩',\n",
       " '床上',\n",
       " '台阶',\n",
       " '张开',\n",
       " '两位',\n",
       " '栏杆',\n",
       " '排球',\n",
       " '头盔',\n",
       " '头',\n",
       " '弯着腰',\n",
       " '前站',\n",
       " '雪地',\n",
       " '杆',\n",
       " '食物',\n",
       " '人来人往',\n",
       " '后面',\n",
       " '墙边',\n",
       " '节目',\n",
       " '办公室',\n",
       " '举着',\n",
       " '下',\n",
       " '黄色',\n",
       " '厨房',\n",
       " '板前',\n",
       " '擂台',\n",
       " '绚丽',\n",
       " '内',\n",
       " '门口',\n",
       " '背景',\n",
       " '短发',\n",
       " '狗',\n",
       " '商店',\n",
       " '双腿',\n",
       " '长裤',\n",
       " '笔',\n",
       " '空地',\n",
       " '叉',\n",
       " '跑步',\n",
       " '球员',\n",
       " '一只',\n",
       " '挽',\n",
       " '腰',\n",
       " '花',\n",
       " '交叉',\n",
       " '吉他',\n",
       " '撑',\n",
       " '衬衫',\n",
       " '奖杯',\n",
       " '玩',\n",
       " '跑',\n",
       " '滑冰',\n",
       " '绿草如茵',\n",
       " '羽毛球',\n",
       " '杯子',\n",
       " '颜色',\n",
       " '呐喊',\n",
       " '滑雪',\n",
       " '橄榄球',\n",
       " '握手',\n",
       " '树林',\n",
       " '昏暗',\n",
       " '小女孩',\n",
       " '共同',\n",
       " '滑冰场',\n",
       " '教室',\n",
       " '左肩',\n",
       " '推',\n",
       " '道',\n",
       " '灰色',\n",
       " '屋外',\n",
       " '骑',\n",
       " '健身房',\n",
       " '男女',\n",
       " '路上',\n",
       " '倚靠在',\n",
       " '玩耍',\n",
       " '比',\n",
       " '骑马',\n",
       " '西服',\n",
       " '打拳击',\n",
       " '翘着',\n",
       " '上身',\n",
       " '自行车',\n",
       " '绿色',\n",
       " '相同',\n",
       " '运动衣',\n",
       " '路边',\n",
       " '一对',\n",
       " '二郎腿',\n",
       " '背心',\n",
       " '凳子',\n",
       " '相机',\n",
       " '阳光',\n",
       " '摘',\n",
       " '左臂',\n",
       " '翠绿',\n",
       " '口罩',\n",
       " '餐厅',\n",
       " '右臂',\n",
       " '戴',\n",
       " '时尚',\n",
       " '长',\n",
       " '举起',\n",
       " '手拿着',\n",
       " '写字',\n",
       " '拥抱',\n",
       " '走廊',\n",
       " '腾空',\n",
       " '袋子',\n",
       " '相互',\n",
       " '田地',\n",
       " '运动装',\n",
       " '土地',\n",
       " '广场',\n",
       " '高尔夫球场',\n",
       " '短',\n",
       " '拍照',\n",
       " '人身',\n",
       " '条纹',\n",
       " '护栏',\n",
       " '房子',\n",
       " '一起',\n",
       " '看书',\n",
       " '吃',\n",
       " '跑道',\n",
       " '海滩',\n",
       " '赤裸',\n",
       " '剪刀手',\n",
       " '棒球',\n",
       " '长裙',\n",
       " '伞',\n",
       " '打电话',\n",
       " '帮',\n",
       " '靠',\n",
       " '拉着',\n",
       " '单手',\n",
       " '比基尼',\n",
       " '整理',\n",
       " '踢球',\n",
       " '保龄球',\n",
       " '地面',\n",
       " '中',\n",
       " '微笑',\n",
       " '手臂',\n",
       " '冰面',\n",
       " '单',\n",
       " '抢球',\n",
       " '同一个',\n",
       " '体育场',\n",
       " '工具',\n",
       " '湖边',\n",
       " '光亮',\n",
       " '病房',\n",
       " '侧身',\n",
       " '行李箱',\n",
       " '低着头',\n",
       " '手牵着',\n",
       " '五个',\n",
       " '石头',\n",
       " '格子',\n",
       " '背',\n",
       " '着手',\n",
       " '玩偶',\n",
       " '平整',\n",
       " '小男孩',\n",
       " '蔬菜',\n",
       " '歪着头',\n",
       " '抬',\n",
       " '书',\n",
       " '斜',\n",
       " '女生',\n",
       " '围裙',\n",
       " '商场',\n",
       " '房间内',\n",
       " '握拳',\n",
       " '车子',\n",
       " '泳池',\n",
       " '交流',\n",
       " '鞋子',\n",
       " '打着',\n",
       " '工作服',\n",
       " '背景墙',\n",
       " '互相',\n",
       " '单膝',\n",
       " '侧',\n",
       " '粉色',\n",
       " '马场',\n",
       " '端',\n",
       " '跃起',\n",
       " '球',\n",
       " '庆祝',\n",
       " '裤兜',\n",
       " '亮堂',\n",
       " '花样滑冰',\n",
       " '系着',\n",
       " '服',\n",
       " '电脑',\n",
       " '果园',\n",
       " '地毯',\n",
       " '围巾',\n",
       " '手握着',\n",
       " '比赛',\n",
       " '古装',\n",
       " '身旁',\n",
       " '安全帽',\n",
       " '制服',\n",
       " '工厂',\n",
       " '大拇指',\n",
       " '趴在',\n",
       " '台上',\n",
       " '小',\n",
       " '胸前',\n",
       " '制作',\n",
       " '戴帽子',\n",
       " '下巴',\n",
       " '捧',\n",
       " '漂亮',\n",
       " '机器',\n",
       " '马甲',\n",
       " '短裙',\n",
       " '手表',\n",
       " '院子',\n",
       " '牌子',\n",
       " '操场上',\n",
       " '医院',\n",
       " '洒满',\n",
       " '公园',\n",
       " '阳光明媚',\n",
       " '瓶子',\n",
       " '打乒乓球',\n",
       " '右腿',\n",
       " '辫子',\n",
       " '网球拍',\n",
       " '船上',\n",
       " '拳套',\n",
       " '戴眼镜',\n",
       " '拳击',\n",
       " '左腿',\n",
       " '屋子',\n",
       " '另',\n",
       " '挥',\n",
       " '扇子',\n",
       " '会议室',\n",
       " '台球',\n",
       " '楼梯',\n",
       " '河边',\n",
       " '肩上',\n",
       " '三位',\n",
       " '泳衣',\n",
       " '模特',\n",
       " '路旁',\n",
       " '外有',\n",
       " '双肩包',\n",
       " '敞亮',\n",
       " '运动',\n",
       " '右',\n",
       " '男',\n",
       " '幕布',\n",
       " '大衣',\n",
       " '地板',\n",
       " '挥杆',\n",
       " '亲吻',\n",
       " '水果',\n",
       " '采访',\n",
       " '环抱',\n",
       " '机舱',\n",
       " '饮料',\n",
       " '阳台',\n",
       " '采摘',\n",
       " '头上',\n",
       " '弯曲',\n",
       " '门前',\n",
       " '杂乱',\n",
       " '打球',\n",
       " '纸',\n",
       " '车厢',\n",
       " '拉',\n",
       " '弹',\n",
       " '超市',\n",
       " '耳机',\n",
       " '托着',\n",
       " '玩具',\n",
       " '旁坐',\n",
       " '同',\n",
       " '讲台',\n",
       " '递',\n",
       " '草丛',\n",
       " '挎包',\n",
       " '拎包',\n",
       " '进行',\n",
       " '麦克风',\n",
       " '面前',\n",
       " '提',\n",
       " '实验室',\n",
       " '锻炼',\n",
       " '握',\n",
       " '提着',\n",
       " '车间',\n",
       " '乐器',\n",
       " '手上',\n",
       " '天空',\n",
       " '马',\n",
       " '白茫茫',\n",
       " '得体',\n",
       " '挂',\n",
       " '铺',\n",
       " '植物',\n",
       " '婚纱',\n",
       " '检查',\n",
       " '酒杯',\n",
       " '画画',\n",
       " '湖面',\n",
       " '服饰',\n",
       " '酒吧',\n",
       " '草原',\n",
       " '摔倒',\n",
       " '挑选',\n",
       " '图书馆',\n",
       " '郁郁葱葱',\n",
       " '膝盖',\n",
       " '纸张',\n",
       " '面对面',\n",
       " '带',\n",
       " '单脚',\n",
       " '草莓',\n",
       " '教',\n",
       " '台子上',\n",
       " '马路边',\n",
       " '沙漠',\n",
       " '牛仔',\n",
       " '演讲',\n",
       " '开会',\n",
       " '女子',\n",
       " '包站',\n",
       " '熙攘',\n",
       " '展厅',\n",
       " '茂盛',\n",
       " '吃饭',\n",
       " '健身',\n",
       " '弯着',\n",
       " '左',\n",
       " '摩托车',\n",
       " '保龄球馆',\n",
       " '练习',\n",
       " '沙地',\n",
       " '长椅',\n",
       " '一望无际',\n",
       " '同一',\n",
       " '下象棋',\n",
       " '箱子',\n",
       " '喂',\n",
       " '两名',\n",
       " '篮子',\n",
       " '动物',\n",
       " '溜冰场',\n",
       " '边站',\n",
       " '无袖',\n",
       " '工地',\n",
       " '瑜伽',\n",
       " '骑着',\n",
       " '轮椅',\n",
       " '光线',\n",
       " '花色',\n",
       " '脚',\n",
       " '病床',\n",
       " '击掌',\n",
       " '一辆',\n",
       " '车',\n",
       " '手势',\n",
       " '围着',\n",
       " '身上',\n",
       " '竖',\n",
       " '冰场',\n",
       " '发布会',\n",
       " '菜地',\n",
       " '石台',\n",
       " '水池',\n",
       " '头巾',\n",
       " '跳跃',\n",
       " '朴素',\n",
       " '实验',\n",
       " '合十',\n",
       " '号',\n",
       " '倒',\n",
       " '红地毯',\n",
       " '白大褂',\n",
       " '游泳池',\n",
       " '蛋糕',\n",
       " '花丛',\n",
       " '一个双',\n",
       " '游泳',\n",
       " '柱子',\n",
       " '医生',\n",
       " '鱼',\n",
       " '跳',\n",
       " '赛场',\n",
       " '物品',\n",
       " '聊天',\n",
       " '木板',\n",
       " '国际象棋',\n",
       " '对视',\n",
       " '车上',\n",
       " '背包',\n",
       " '围栏',\n",
       " '球场',\n",
       " '蓝天',\n",
       " '边有',\n",
       " '秀',\n",
       " '热闹',\n",
       " '白雪皑皑',\n",
       " '一条',\n",
       " '牵着手',\n",
       " '灌篮',\n",
       " '拍摄',\n",
       " '嘴',\n",
       " '锻炼身体',\n",
       " '桌球',\n",
       " '婴儿车',\n",
       " '茂密',\n",
       " '服装',\n",
       " '馆里',\n",
       " '小孩子',\n",
       " '可爱',\n",
       " '一个男孩',\n",
       " '道具',\n",
       " '紫色',\n",
       " '棚子',\n",
       " '大人',\n",
       " '脸',\n",
       " '玩游戏',\n",
       " '伸着',\n",
       " '领带',\n",
       " '扛着',\n",
       " '鼓掌',\n",
       " '欢呼',\n",
       " '体育馆',\n",
       " '餐桌',\n",
       " '场里',\n",
       " '脖子',\n",
       " '田野',\n",
       " '帐篷',\n",
       " '屋内',\n",
       " '眼睛',\n",
       " '冰球',\n",
       " '洁净',\n",
       " '单肩',\n",
       " '空旷',\n",
       " '理发店',\n",
       " '高跟鞋',\n",
       " '演出服',\n",
       " '一匹',\n",
       " '签名',\n",
       " '扔',\n",
       " '石阶',\n",
       " '胸',\n",
       " '清澈',\n",
       " '墙壁',\n",
       " '拍',\n",
       " '墙上',\n",
       " '扭着',\n",
       " '商品',\n",
       " '右脚',\n",
       " '夹',\n",
       " '充足',\n",
       " '飞机',\n",
       " '洒',\n",
       " '河水',\n",
       " '广告',\n",
       " '脸颊',\n",
       " '插进',\n",
       " '水里',\n",
       " '救生衣',\n",
       " '单腿',\n",
       " '大树',\n",
       " '本子',\n",
       " '筷子',\n",
       " '破旧',\n",
       " '垫子',\n",
       " '训练',\n",
       " '菜园',\n",
       " '肩膀',\n",
       " '盘子',\n",
       " '半',\n",
       " '窗户',\n",
       " '盘着',\n",
       " '左脚',\n",
       " '低头',\n",
       " '鲜花',\n",
       " '跟',\n",
       " '菜',\n",
       " '碗',\n",
       " '布前',\n",
       " '男生',\n",
       " '喝水',\n",
       " '起',\n",
       " '手牵手',\n",
       " '身体',\n",
       " '观众席',\n",
       " '盒子',\n",
       " '游戏',\n",
       " '投篮',\n",
       " '婴儿',\n",
       " '刀',\n",
       " '踩',\n",
       " '竖起',\n",
       " '棍子',\n",
       " '往',\n",
       " '桌旁',\n",
       " '游乐场',\n",
       " '地里',\n",
       " '璀璨',\n",
       " '披肩',\n",
       " '医护人员',\n",
       " '绳子',\n",
       " '手扶',\n",
       " '靠着',\n",
       " '绿植旁',\n",
       " '摄像机',\n",
       " '按摩',\n",
       " '向',\n",
       " '划船',\n",
       " '露台',\n",
       " '推车',\n",
       " '店铺',\n",
       " '桶',\n",
       " '草丛里',\n",
       " '笑容满面',\n",
       " '天',\n",
       " '闭着',\n",
       " '笔记本电脑',\n",
       " '琳琅满目',\n",
       " '放',\n",
       " '小道',\n",
       " '各自',\n",
       " '海面',\n",
       " '同样',\n",
       " '笑',\n",
       " '嘴边',\n",
       " '光滑',\n",
       " '树丛',\n",
       " '操作',\n",
       " '拄着',\n",
       " '喝',\n",
       " '双脚',\n",
       " '推着',\n",
       " '弯',\n",
       " '载',\n",
       " '跨栏',\n",
       " '拐杖',\n",
       " '光',\n",
       " '蔚蓝',\n",
       " '帅气',\n",
       " '发言',\n",
       " '牛',\n",
       " '水',\n",
       " '毛笔',\n",
       " '树枝',\n",
       " '垃圾',\n",
       " '冰壶',\n",
       " '一张',\n",
       " '马背上',\n",
       " '指着',\n",
       " '路面',\n",
       " '西瓜',\n",
       " '背后',\n",
       " '理发',\n",
       " '球馆',\n",
       " '勺子',\n",
       " '书架',\n",
       " '统一',\n",
       " '简洁',\n",
       " '一',\n",
       " '男子',\n",
       " '湿漉漉',\n",
       " '海里',\n",
       " '橘色',\n",
       " '擦',\n",
       " '操作电脑',\n",
       " '并排',\n",
       " '台球室',\n",
       " '切',\n",
       " '铁锹',\n",
       " '滑板',\n",
       " '户外',\n",
       " '争',\n",
       " '盘腿',\n",
       " '波光粼粼',\n",
       " '柜台',\n",
       " '耳麦',\n",
       " '睡觉',\n",
       " '潮湿',\n",
       " '杆子',\n",
       " '大褂',\n",
       " '一名',\n",
       " '照耀',\n",
       " '柔软',\n",
       " '护士',\n",
       " '场上',\n",
       " '剪刀',\n",
       " '兜里',\n",
       " '讲解',\n",
       " '烧烤',\n",
       " '伸出',\n",
       " 't恤',\n",
       " '额头',\n",
       " '简陋',\n",
       " '牵',\n",
       " '头顶',\n",
       " '中有',\n",
       " '上篮',\n",
       " '讲课',\n",
       " '绿意盎然',\n",
       " '摆放',\n",
       " '屏幕',\n",
       " '大街',\n",
       " '厨师',\n",
       " '修理',\n",
       " '练',\n",
       " '文件',\n",
       " '溜冰',\n",
       " '撩',\n",
       " '弯腰',\n",
       " '食指',\n",
       " '长凳',\n",
       " '证书',\n",
       " '简朴',\n",
       " '正在',\n",
       " '台',\n",
       " '优雅',\n",
       " '亭子',\n",
       " '生机勃勃',\n",
       " '毛衣',\n",
       " '座椅',\n",
       " '举',\n",
       " '裁判',\n",
       " '橙色',\n",
       " '旗袍',\n",
       " '捡',\n",
       " '弹钢琴',\n",
       " '一头',\n",
       " '柜子',\n",
       " '对',\n",
       " '发箍',\n",
       " '十指',\n",
       " '花束',\n",
       " '照相机',\n",
       " '岩石',\n",
       " '大树下',\n",
       " '卧室',\n",
       " '一件',\n",
       " '间',\n",
       " '跷着',\n",
       " '讨论',\n",
       " '用',\n",
       " '环绕',\n",
       " '洁白',\n",
       " '捂着',\n",
       " '床边',\n",
       " '身材',\n",
       " '裙摆',\n",
       " '皮肤',\n",
       " '海水',\n",
       " '报纸',\n",
       " '托',\n",
       " '台前',\n",
       " '马尾',\n",
       " '照射',\n",
       " '拳击台',\n",
       " '张着',\n",
       " '倒水',\n",
       " '黝黑',\n",
       " '摆满',\n",
       " '平板',\n",
       " '外站',\n",
       " '写',\n",
       " '书店',\n",
       " '趴着',\n",
       " '网球场',\n",
       " '演奏',\n",
       " '滑雪场',\n",
       " '浇水',\n",
       " '接吻',\n",
       " '指导',\n",
       " '抚摸',\n",
       " '弹着',\n",
       " '垃圾场',\n",
       " '使用',\n",
       " '骑着马',\n",
       " '暗淡',\n",
       " '手站',\n",
       " '坐满',\n",
       " '切菜',\n",
       " '人站',\n",
       " '人手',\n",
       " '交叉着',\n",
       " '书本',\n",
       " '下车',\n",
       " '草帽',\n",
       " '棕色',\n",
       " '桥',\n",
       " '亮起',\n",
       " '骑车',\n",
       " '车水马龙',\n",
       " '腰站',\n",
       " '手工',\n",
       " '观众',\n",
       " '羽毛球拍',\n",
       " '漂流',\n",
       " '酒',\n",
       " '赤着',\n",
       " '毛巾',\n",
       " '挥手',\n",
       " '嘴巴',\n",
       " '做饭',\n",
       " '雨伞',\n",
       " '跳绳',\n",
       " '羊',\n",
       " '病人',\n",
       " '灭火',\n",
       " '滑梯',\n",
       " '披着',\n",
       " '小提琴',\n",
       " '争球',\n",
       " '万众瞩目',\n",
       " '玻璃',\n",
       " '水面',\n",
       " '交叠',\n",
       " '转头',\n",
       " '西装革履',\n",
       " '西红柿',\n",
       " '红领巾',\n",
       " '枝繁叶茂',\n",
       " '挑着',\n",
       " '头饰',\n",
       " '耳朵',\n",
       " '旗子',\n",
       " '扶手',\n",
       " '房门',\n",
       " '逗',\n",
       " '花坛',\n",
       " '肚子',\n",
       " '皮艇',\n",
       " '水边',\n",
       " '棋盘',\n",
       " '摊位',\n",
       " '鸽子',\n",
       " '闪烁',\n",
       " '绿树',\n",
       " '电梯',\n",
       " '池塘',\n",
       " '比划',\n",
       " '模型',\n",
       " '树下',\n",
       " '捏',\n",
       " '山坡',\n",
       " '展示',\n",
       " '小狗',\n",
       " '体操',\n",
       " '下棋',\n",
       " '铁轨',\n",
       " '购物车',\n",
       " '货架',\n",
       " '被',\n",
       " '温馨',\n",
       " '武术',\n",
       " '头戴',\n",
       " '剪纸',\n",
       " '钢琴',\n",
       " '自拍',\n",
       " '背上',\n",
       " '笔直',\n",
       " '画',\n",
       " '一块',\n",
       " '阶梯',\n",
       " '身子',\n",
       " '跆拳道',\n",
       " '缰绳',\n",
       " '猫',\n",
       " '整齐',\n",
       " '准备',\n",
       " '绿树成荫',\n",
       " '玩具车',\n",
       " '气球',\n",
       " '杖',\n",
       " '打拳',\n",
       " '围棋',\n",
       " '压腿',\n",
       " '做菜',\n",
       " '仰着',\n",
       " '马上',\n",
       " '调酒',\n",
       " '落叶',\n",
       " '花园里',\n",
       " '舞蹈',\n",
       " '玩电脑',\n",
       " '湍急',\n",
       " '清理',\n",
       " '和服',\n",
       " '雕塑',\n",
       " '脸上',\n",
       " '灭火器',\n",
       " '河里',\n",
       " '斜挎包',\n",
       " '手术室',\n",
       " '宽敞明亮',\n",
       " '几个',\n",
       " '人山人海',\n",
       " '钓鱼',\n",
       " '狭窄',\n",
       " '熙熙攘攘',\n",
       " '游泳馆',\n",
       " '游泳圈',\n",
       " ...]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有词汇\n",
    "vectorization.get_vocabulary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4428f2c8",
   "metadata": {},
   "source": [
    "上方则是出现过的所有词，按照频率排序，索引0为空，1为未登录进来的词。\n",
    "\n",
    "假如我们自己随便写句话：一个男人在打游戏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "26cba029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 25), dtype=int64, numpy=\n",
       "array([[  5,   8,   6,  67, 687,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
       "      dtype=int64)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorization(['一个 男人 在 打 游戏'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b32ce9d",
   "metadata": {},
   "source": [
    "可以看到 '一个 男人 在 打 游戏' 这五个词的索引分别是 5，8，6，687 其中0代表补白(当然也可以看作索引0的空)，如果句子短不到25就用0补充，超过25了就截断。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c54504b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'我 在 准备 考研'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str = \" \".join([i for i in jieba.cut('我在准备考研')])\n",
    "str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8896906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 25), dtype=int64, numpy=\n",
       "array([[7853,    6,  967,    1,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0]], dtype=int64)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorization([str]) # 考研二字未登录在词典中 索引为1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9865fceb",
   "metadata": {},
   "source": [
    "解码操作，vocab[向量]  如 vocab[[1,2,3]] 会得根据索引到相应的话"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e078459b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = np.array(vectorization.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d02dffc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['一个', '男人', '在', '打', '游戏'], dtype='<U7')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[[5,8,6,67,687]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "952cdbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['我', '在', '准备', '[UNK]'], dtype='<U7')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[[7853,6,967,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2b999a",
   "metadata": {},
   "source": [
    "### 制作数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccb39d5",
   "metadata": {},
   "source": [
    "这一步 我们要把train_data和valid_data这两个字典中的图片进行压缩resize 生成准备使用的数据集格式\n",
    "\n",
    "+ tf.data.Dataset.from_tensor_slices() 该函数的作用是接收tensor，对tensor的第一维度进行切分，并返回一个表示该tensor的切片数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88b76692",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_and_resize(img_path):\n",
    "    # 读取图片，并缩放\n",
    "    img = tf.io.read_file(img_path)\n",
    "    img = tf.image.decode_jpeg(img, channels=3)\n",
    "    img = tf.image.resize(img, IMAGE_SIZE)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    return img\n",
    "\n",
    "\n",
    "def process_input(img_path, captions):\n",
    "    return decode_and_resize(img_path), vectorization(captions)\n",
    "\n",
    "\n",
    "def make_dataset(images, captions):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((images, captions))\n",
    "    dataset = dataset.shuffle(len(images))\n",
    "    dataset = dataset.map(process_input, num_parallel_calls=AUTOTUNE)\n",
    "    dataset = dataset.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n",
    "\n",
    "    return dataset\n",
    "    \n",
    "    \n",
    "# 制作数据集\n",
    "train_dataset = make_dataset(list(train_data.keys()), list(train_data.values()))\n",
    "valid_dataset = make_dataset(list(valid_data.keys()), list(valid_data.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24e651b",
   "metadata": {},
   "source": [
    "接下来我们从train_dataset中拿出数据来看一下到底是什么，因为它是一个可迭代对象，一批有64个(batch个)，一共23896/64=374批只看一个就可以，所以记得加上break。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f93d1f",
   "metadata": {},
   "source": [
    "可以看到输入图片的大小是 299x299x3  后方词向量caption有5个，每个长25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0cbdb44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 299, 299, 3)\n",
      "(64, 5, 25)\n"
     ]
    }
   ],
   "source": [
    "for i in train_dataset:\n",
    "    print(i[0].shape)\n",
    "    print(i[1].shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c555e293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['<start>' '两个' '双手' '放在' '身前' '的' '男人' '和' '一个' '戴着' '眼镜' '的' '男人' '站'\n",
      "  '在' '大厅' '里' '<end>' '' '' '' '' '' '' '']\n",
      " ['<start>' '两个' '戴着' '帽子' '的' '男人' '旁边' '有' '一个' '双手' '放在' '身前' '的' '男人'\n",
      "  '站' '在' '大厅' '里' '<end>' '' '' '' '' '' '']\n",
      " ['<start>' '两个' '戴着' '帽子' '的' '人' '和' '一个' '短' '头发' '的' '男人' '站' '在'\n",
      "  '室内' '<end>' '' '' '' '' '' '' '' '' '']\n",
      " ['<start>' '两个' '双手' '放在' '身前' '的' '男人' '和' '一个' '戴着' '眼镜' '的' '男人' '站'\n",
      "  '在' '商场' '里' '<end>' '' '' '' '' '' '' '']\n",
      " ['<start>' '一个' '人旁' '有' '两个' '戴着' '帽子' '的' '人' '站' '在' '室外' '<end>' ''\n",
      "  '' '' '' '' '' '' '' '' '' '' '']]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in train_dataset:\n",
    "    # 获取图片\n",
    "    img = i[0][0].numpy().astype('int')\n",
    "    # 获取标注(词向量)\n",
    "    caption = i[1][0].numpy()\n",
    "    # 显示\n",
    "    plt.imshow(img)\n",
    "    # 解码\n",
    "    print(vocab[caption])\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48261f9",
   "metadata": {},
   "source": [
    "### 数据增强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e8a75404",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据增强\n",
    "image_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
    "        layers.experimental.preprocessing.RandomRotation(0.2),\n",
    "        layers.experimental.preprocessing.RandomContrast(0.3),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ba98f6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c4314d00",
   "metadata": {},
   "source": [
    "## 构建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d02c8e21",
   "metadata": {},
   "source": [
    "### 1.构建CNN提取图片特征"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9cd1c78",
   "metadata": {},
   "source": [
    "方便起见，这里选择使用 EfficientNet 和 迁移学习 的方式来完成\n",
    "\n",
    "其中 EfficientNet由16个移动翻转瓶颈卷积模块，2个卷积层，1个全局平均池化层和1个分类层构成。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cdd93087",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cnn_model():\n",
    "    # CNN模型\n",
    "    base_model = efficientnet.EfficientNetB0(\n",
    "        input_shape=(*IMAGE_SIZE, 3), include_top=False, weights=\"imagenet\",\n",
    "    )\n",
    "    # 冻住特征提取层\n",
    "    base_model.trainable = False\n",
    "    base_model_out = base_model.output\n",
    "    # 我们要修改输出层，(n,100,1280)\n",
    "    base_model_out = layers.Reshape((-1, base_model_out.shape[-1]))(base_model_out)\n",
    "    cnn_model = keras.models.Model(base_model.input, base_model_out)\n",
    "    return cnn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a9f82c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "rescaling (Rescaling)           (None, 299, 299, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "normalization (Normalization)   (None, 299, 299, 3)  7           rescaling[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv_pad (ZeroPadding2D)   (None, 301, 301, 3)  0           normalization[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv (Conv2D)              (None, 150, 150, 32) 864         stem_conv_pad[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_bn (BatchNormalization)    (None, 150, 150, 32) 128         stem_conv[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_activation (Activation)    (None, 150, 150, 32) 0           stem_bn[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1a_dwconv (DepthwiseConv2D (None, 150, 150, 32) 288         stem_activation[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1a_bn (BatchNormalization) (None, 150, 150, 32) 128         block1a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1a_activation (Activation) (None, 150, 150, 32) 0           block1a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_excite (Multiply)    (None, 150, 150, 32) 0           block1a_activation[0][0]         \n",
      "                                                                 block1a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_conv (Conv2D)   (None, 150, 150, 16) 512         block1a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_bn (BatchNormal (None, 150, 150, 16) 64          block1a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_conv (Conv2D)    (None, 150, 150, 96) 1536        block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_bn (BatchNormali (None, 150, 150, 96) 384         block2a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_activation (Acti (None, 150, 150, 96) 0           block2a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv_pad (ZeroPadding (None, 151, 151, 96) 0           block2a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv (DepthwiseConv2D (None, 75, 75, 96)   864         block2a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_bn (BatchNormalization) (None, 75, 75, 96)   384         block2a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2a_activation (Activation) (None, 75, 75, 96)   0           block2a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_excite (Multiply)    (None, 75, 75, 96)   0           block2a_activation[0][0]         \n",
      "                                                                 block2a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_conv (Conv2D)   (None, 75, 75, 24)   2304        block2a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_bn (BatchNormal (None, 75, 75, 24)   96          block2a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_conv (Conv2D)    (None, 75, 75, 144)  3456        block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_bn (BatchNormali (None, 75, 75, 144)  576         block2b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_activation (Acti (None, 75, 75, 144)  0           block2b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_dwconv (DepthwiseConv2D (None, 75, 75, 144)  1296        block2b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2b_bn (BatchNormalization) (None, 75, 75, 144)  576         block2b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2b_activation (Activation) (None, 75, 75, 144)  0           block2b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_excite (Multiply)    (None, 75, 75, 144)  0           block2b_activation[0][0]         \n",
      "                                                                 block2b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_conv (Conv2D)   (None, 75, 75, 24)   3456        block2b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_bn (BatchNormal (None, 75, 75, 24)   96          block2b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_drop (Dropout)          (None, 75, 75, 24)   0           block2b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_add (Add)               (None, 75, 75, 24)   0           block2b_drop[0][0]               \n",
      "                                                                 block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_conv (Conv2D)    (None, 75, 75, 144)  3456        block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_bn (BatchNormali (None, 75, 75, 144)  576         block3a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_activation (Acti (None, 75, 75, 144)  0           block3a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv_pad (ZeroPadding (None, 79, 79, 144)  0           block3a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv (DepthwiseConv2D (None, 38, 38, 144)  3600        block3a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_bn (BatchNormalization) (None, 38, 38, 144)  576         block3a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3a_activation (Activation) (None, 38, 38, 144)  0           block3a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_excite (Multiply)    (None, 38, 38, 144)  0           block3a_activation[0][0]         \n",
      "                                                                 block3a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_conv (Conv2D)   (None, 38, 38, 40)   5760        block3a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_bn (BatchNormal (None, 38, 38, 40)   160         block3a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_conv (Conv2D)    (None, 38, 38, 240)  9600        block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_bn (BatchNormali (None, 38, 38, 240)  960         block3b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_activation (Acti (None, 38, 38, 240)  0           block3b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_dwconv (DepthwiseConv2D (None, 38, 38, 240)  6000        block3b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3b_bn (BatchNormalization) (None, 38, 38, 240)  960         block3b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3b_activation (Activation) (None, 38, 38, 240)  0           block3b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_excite (Multiply)    (None, 38, 38, 240)  0           block3b_activation[0][0]         \n",
      "                                                                 block3b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_conv (Conv2D)   (None, 38, 38, 40)   9600        block3b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_bn (BatchNormal (None, 38, 38, 40)   160         block3b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_drop (Dropout)          (None, 38, 38, 40)   0           block3b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_add (Add)               (None, 38, 38, 40)   0           block3b_drop[0][0]               \n",
      "                                                                 block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_conv (Conv2D)    (None, 38, 38, 240)  9600        block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_bn (BatchNormali (None, 38, 38, 240)  960         block4a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_activation (Acti (None, 38, 38, 240)  0           block4a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv_pad (ZeroPadding (None, 39, 39, 240)  0           block4a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv (DepthwiseConv2D (None, 19, 19, 240)  2160        block4a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_bn (BatchNormalization) (None, 19, 19, 240)  960         block4a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4a_activation (Activation) (None, 19, 19, 240)  0           block4a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_excite (Multiply)    (None, 19, 19, 240)  0           block4a_activation[0][0]         \n",
      "                                                                 block4a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_conv (Conv2D)   (None, 19, 19, 80)   19200       block4a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_bn (BatchNormal (None, 19, 19, 80)   320         block4a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_conv (Conv2D)    (None, 19, 19, 480)  38400       block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_bn (BatchNormali (None, 19, 19, 480)  1920        block4b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_activation (Acti (None, 19, 19, 480)  0           block4b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_dwconv (DepthwiseConv2D (None, 19, 19, 480)  4320        block4b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4b_bn (BatchNormalization) (None, 19, 19, 480)  1920        block4b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4b_activation (Activation) (None, 19, 19, 480)  0           block4b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_excite (Multiply)    (None, 19, 19, 480)  0           block4b_activation[0][0]         \n",
      "                                                                 block4b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_conv (Conv2D)   (None, 19, 19, 80)   38400       block4b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_bn (BatchNormal (None, 19, 19, 80)   320         block4b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_drop (Dropout)          (None, 19, 19, 80)   0           block4b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_add (Add)               (None, 19, 19, 80)   0           block4b_drop[0][0]               \n",
      "                                                                 block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_conv (Conv2D)    (None, 19, 19, 480)  38400       block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_bn (BatchNormali (None, 19, 19, 480)  1920        block4c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_activation (Acti (None, 19, 19, 480)  0           block4c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_dwconv (DepthwiseConv2D (None, 19, 19, 480)  4320        block4c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4c_bn (BatchNormalization) (None, 19, 19, 480)  1920        block4c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4c_activation (Activation) (None, 19, 19, 480)  0           block4c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_excite (Multiply)    (None, 19, 19, 480)  0           block4c_activation[0][0]         \n",
      "                                                                 block4c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_conv (Conv2D)   (None, 19, 19, 80)   38400       block4c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_bn (BatchNormal (None, 19, 19, 80)   320         block4c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4c_drop (Dropout)          (None, 19, 19, 80)   0           block4c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_add (Add)               (None, 19, 19, 80)   0           block4c_drop[0][0]               \n",
      "                                                                 block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_conv (Conv2D)    (None, 19, 19, 480)  38400       block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_bn (BatchNormali (None, 19, 19, 480)  1920        block5a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_activation (Acti (None, 19, 19, 480)  0           block5a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_dwconv (DepthwiseConv2D (None, 19, 19, 480)  12000       block5a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5a_bn (BatchNormalization) (None, 19, 19, 480)  1920        block5a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5a_activation (Activation) (None, 19, 19, 480)  0           block5a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_excite (Multiply)    (None, 19, 19, 480)  0           block5a_activation[0][0]         \n",
      "                                                                 block5a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_conv (Conv2D)   (None, 19, 19, 112)  53760       block5a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_bn (BatchNormal (None, 19, 19, 112)  448         block5a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_conv (Conv2D)    (None, 19, 19, 672)  75264       block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_bn (BatchNormali (None, 19, 19, 672)  2688        block5b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_activation (Acti (None, 19, 19, 672)  0           block5b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_dwconv (DepthwiseConv2D (None, 19, 19, 672)  16800       block5b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5b_bn (BatchNormalization) (None, 19, 19, 672)  2688        block5b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5b_activation (Activation) (None, 19, 19, 672)  0           block5b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_excite (Multiply)    (None, 19, 19, 672)  0           block5b_activation[0][0]         \n",
      "                                                                 block5b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_conv (Conv2D)   (None, 19, 19, 112)  75264       block5b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_bn (BatchNormal (None, 19, 19, 112)  448         block5b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_drop (Dropout)          (None, 19, 19, 112)  0           block5b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_add (Add)               (None, 19, 19, 112)  0           block5b_drop[0][0]               \n",
      "                                                                 block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_conv (Conv2D)    (None, 19, 19, 672)  75264       block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_bn (BatchNormali (None, 19, 19, 672)  2688        block5c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_activation (Acti (None, 19, 19, 672)  0           block5c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_dwconv (DepthwiseConv2D (None, 19, 19, 672)  16800       block5c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5c_bn (BatchNormalization) (None, 19, 19, 672)  2688        block5c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5c_activation (Activation) (None, 19, 19, 672)  0           block5c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_excite (Multiply)    (None, 19, 19, 672)  0           block5c_activation[0][0]         \n",
      "                                                                 block5c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_conv (Conv2D)   (None, 19, 19, 112)  75264       block5c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_bn (BatchNormal (None, 19, 19, 112)  448         block5c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5c_drop (Dropout)          (None, 19, 19, 112)  0           block5c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_add (Add)               (None, 19, 19, 112)  0           block5c_drop[0][0]               \n",
      "                                                                 block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_conv (Conv2D)    (None, 19, 19, 672)  75264       block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_bn (BatchNormali (None, 19, 19, 672)  2688        block6a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_activation (Acti (None, 19, 19, 672)  0           block6a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv_pad (ZeroPadding (None, 23, 23, 672)  0           block6a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv (DepthwiseConv2D (None, 10, 10, 672)  16800       block6a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_bn (BatchNormalization) (None, 10, 10, 672)  2688        block6a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6a_activation (Activation) (None, 10, 10, 672)  0           block6a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_excite (Multiply)    (None, 10, 10, 672)  0           block6a_activation[0][0]         \n",
      "                                                                 block6a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_conv (Conv2D)   (None, 10, 10, 192)  129024      block6a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_bn (BatchNormal (None, 10, 10, 192)  768         block6a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block6b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_activation (Acti (None, 10, 10, 1152) 0           block6b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 28800       block6b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6b_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block6b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6b_activation (Activation) (None, 10, 10, 1152) 0           block6b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_excite (Multiply)    (None, 10, 10, 1152) 0           block6b_activation[0][0]         \n",
      "                                                                 block6b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_conv (Conv2D)   (None, 10, 10, 192)  221184      block6b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_bn (BatchNormal (None, 10, 10, 192)  768         block6b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_drop (Dropout)          (None, 10, 10, 192)  0           block6b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_add (Add)               (None, 10, 10, 192)  0           block6b_drop[0][0]               \n",
      "                                                                 block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block6c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_activation (Acti (None, 10, 10, 1152) 0           block6c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 28800       block6c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6c_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block6c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6c_activation (Activation) (None, 10, 10, 1152) 0           block6c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_excite (Multiply)    (None, 10, 10, 1152) 0           block6c_activation[0][0]         \n",
      "                                                                 block6c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_conv (Conv2D)   (None, 10, 10, 192)  221184      block6c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_bn (BatchNormal (None, 10, 10, 192)  768         block6c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6c_drop (Dropout)          (None, 10, 10, 192)  0           block6c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_add (Add)               (None, 10, 10, 192)  0           block6c_drop[0][0]               \n",
      "                                                                 block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block6d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_activation (Acti (None, 10, 10, 1152) 0           block6d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 28800       block6d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6d_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block6d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6d_activation (Activation) (None, 10, 10, 1152) 0           block6d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_excite (Multiply)    (None, 10, 10, 1152) 0           block6d_activation[0][0]         \n",
      "                                                                 block6d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_conv (Conv2D)   (None, 10, 10, 192)  221184      block6d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_bn (BatchNormal (None, 10, 10, 192)  768         block6d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6d_drop (Dropout)          (None, 10, 10, 192)  0           block6d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_add (Add)               (None, 10, 10, 192)  0           block6d_drop[0][0]               \n",
      "                                                                 block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block7a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_activation (Acti (None, 10, 10, 1152) 0           block7a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 10368       block7a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7a_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block7a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7a_activation (Activation) (None, 10, 10, 1152) 0           block7a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_excite (Multiply)    (None, 10, 10, 1152) 0           block7a_activation[0][0]         \n",
      "                                                                 block7a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_conv (Conv2D)   (None, 10, 10, 320)  368640      block7a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_bn (BatchNormal (None, 10, 10, 320)  1280        block7a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "top_conv (Conv2D)               (None, 10, 10, 1280) 409600      block7a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "top_bn (BatchNormalization)     (None, 10, 10, 1280) 5120        top_conv[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "top_activation (Activation)     (None, 10, 10, 1280) 0           top_bn[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 100, 1280)    0           top_activation[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 4,049,571\n",
      "Trainable params: 0\n",
      "Non-trainable params: 4,049,571\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_model = get_cnn_model()\n",
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189dde1e",
   "metadata": {},
   "source": [
    "这样 当我们每输入一批/一个batch个数据时，就会输出一批/一个batch个数据：\n",
    "\n",
    "即输入 64x299x299x3的图片 输出64个图片的特征，维度是100x1280"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41c4649",
   "metadata": {},
   "source": [
    "**测试一下CNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ac395e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 100, 1280])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模拟图片测试一下\n",
    "cnn_test_input = tf.random.normal([64, 299,299,3]) # 随机正态分布64张299x299x3的图片\n",
    "# 输入网络\n",
    "cnn_test_output = cnn_model(cnn_test_input, training=False)\n",
    "cnn_test_output.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e366220a",
   "metadata": {},
   "source": [
    "### 2.构建编码器transformer encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0e4202b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerEncoderBlock(layers.Layer):\n",
    "    # transformer encoder网络：https://www.youtube.com/watch?v=n9TlOhRjYoc\n",
    "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
    "        super().__init__()\n",
    "        self.embed_dim = embed_dim\n",
    "        self.dense_dim = dense_dim\n",
    "        self.num_heads = num_heads\n",
    "        self.attention_1 = layers.MultiHeadAttention( # multi head attention\n",
    "            num_heads=num_heads, key_dim=embed_dim, dropout=0.0\n",
    "        ) # 头的数量  输出维度的大小  dropout\n",
    "        self.layernorm_1 = layers.LayerNormalization()\n",
    "        self.layernorm_2 = layers.LayerNormalization()\n",
    "        self.dense_1 = layers.Dense(embed_dim, activation=\"relu\")\n",
    "\n",
    "    def call(self, inputs, training, mask=None):\n",
    "        # layer norm\n",
    "        inputs = self.layernorm_1(inputs)\n",
    "        inputs = self.dense_1(inputs)\n",
    "        \n",
    "        # 传入 q k v\n",
    "        attention_output_1 = self.attention_1(\n",
    "            query=inputs,\n",
    "            value=inputs,\n",
    "            key=inputs,\n",
    "            attention_mask=None,\n",
    "            training=training, # training：布尔值，表示推理还是训练（是否使用 dropout）\n",
    "        )\n",
    "        # residual然后再layer norm\n",
    "        out_1 = self.layernorm_2(inputs + attention_output_1) # 残差链接\n",
    "        return out_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c541f4",
   "metadata": {},
   "source": [
    "**测试一下transformer encoder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1a525272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 100, 512])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试一下  我们把CNN的特征值给它\n",
    "encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1)\n",
    "# 输入网络\n",
    "encoder_test_output = encoder(cnn_test_output, training=False)\n",
    "encoder_test_output.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0edba0a5",
   "metadata": {},
   "source": [
    "### 3. 位置编码Positional Embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf45ea8",
   "metadata": {},
   "source": [
    "**这一步做的是：** 每张图片对应5个词向量，我们选出一个去掉最后的\"<end>\"(此时长为24了)，之前比如词向量是[1,6,4,2,0,0,...,0] 现在对其进行升维，比如用一个二维(该项目升维512)坐标分别表示1，6，4，...，\n",
    "\n",
    "如： 1->[0.1, 4.2]     6->[4.1, 2.0] ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f6c2b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbedding(layers.Layer):\n",
    "    # 位置编码\n",
    "    def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):\n",
    "        super().__init__()\n",
    "        '''\n",
    "        embedding用法：https://stats.stackexchange.com/questions/270546/how-does-keras-embedding-layer-work\n",
    "        input_dim：词汇数量；output_dim：特征向量大小\n",
    "        '''\n",
    "        # token embedding：长度为vocab_size，特征向量为：embed_dim\n",
    "        self.token_embeddings = layers.Embedding(\n",
    "            input_dim=vocab_size, output_dim=embed_dim\n",
    "        )\n",
    "        # position_embeddings：\n",
    "        self.position_embeddings = layers.Embedding(\n",
    "            input_dim=sequence_length, output_dim=embed_dim\n",
    "        )\n",
    "        self.sequence_length = sequence_length\n",
    "        self.vocab_size = vocab_size\n",
    "        self.embed_dim = embed_dim\n",
    "\n",
    "        # 512开根号：22.627416998：https://jalammar.github.io/illustrated-transformer/\n",
    "        self.embed_scale = tf.math.sqrt(tf.cast(embed_dim, tf.float32))\n",
    "\n",
    "\n",
    "    def call(self, inputs):\n",
    "        # 获取caption长度，这里是24个（前24个单词，去掉<end>）\n",
    "        length = tf.shape(inputs)[-1]\n",
    "\n",
    "        # 生成0~length(即24)的数字\n",
    "        positions = tf.range(start=0, limit=length, delta=1)\n",
    "        \n",
    "        # 输入的句子index转为embedding特征，大小：(N, 24, 512)\n",
    "        embedded_tokens = self.token_embeddings(inputs)\n",
    "        # 乘以22.62  上面开根号了 这里乘过去 反向传播好算\n",
    "        embedded_tokens = embedded_tokens * self.embed_scale\n",
    "        \n",
    "        # 位置编码，大小：(24, 512)\n",
    "        embedded_positions = self.position_embeddings(positions)\n",
    "        \n",
    "        # 加和 返回\n",
    "        return embedded_tokens + embedded_positions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad6b314",
   "metadata": {},
   "source": [
    "**测试一下PositionalEmbedding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5604d692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 24)\n",
      "(64, 24, 512)\n"
     ]
    }
   ],
   "source": [
    "# 测试模型\n",
    "test_embedding_model = PositionalEmbedding(embed_dim=EMBED_DIM, sequence_length=SEQ_LENGTH, vocab_size=VOCAB_SIZE)\n",
    "\n",
    "# 测试输入，选择一个batch中的第一个句子（一共有5个）\n",
    "for i in train_dataset:\n",
    "    # 获取测试标签中的一个的前24个词 大小(64, 24)\n",
    "    caption = i[1][:,0,:-1]\n",
    "    print(caption.shape)\n",
    "    # 传入模型\n",
    "    positional_output = test_embedding_model(caption)\n",
    "    # 打印结果的大小\n",
    "    print(positional_output.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3775562",
   "metadata": {},
   "source": [
    "强制教学，原来是64x24x1 现在是(64, 24, 512) 简单来说就是词向量映射到高维了\n",
    "\n",
    "长度为24的单词变为512的embedding向量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc5a53c",
   "metadata": {},
   "source": [
    "### 4.构建解码器transformer decoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9231758e",
   "metadata": {},
   "source": [
    "这里不懂的自己查一下吧 三言两语说不完\n",
    "\n",
    "总之 这一步最后输出为VOCAB_SIZE(这里为1000)大小的向量，对应位置的大小为概率，可以查索引来获取相应原单词，比如变成(64, 24, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "efadca28",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerDecoderBlock(layers.Layer):\n",
    "    \n",
    "    def __init__(self, embed_dim, ff_dim, num_heads, **kwargs):\n",
    "        super().__init__()\n",
    "        self.embed_dim = embed_dim\n",
    "        self.ff_dim = ff_dim\n",
    "        self.num_heads = num_heads\n",
    "        self.attention_1 = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=embed_dim, dropout=0.1\n",
    "        )\n",
    "        self.attention_2 = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=embed_dim, dropout=0.1\n",
    "        )\n",
    "        self.ffn_layer_1 = layers.Dense(ff_dim, activation=\"relu\")\n",
    "        self.ffn_layer_2 = layers.Dense(embed_dim)\n",
    "\n",
    "        self.layernorm_1 = layers.LayerNormalization()\n",
    "        self.layernorm_2 = layers.LayerNormalization()\n",
    "        self.layernorm_3 = layers.LayerNormalization()\n",
    "        \n",
    "        # 位置编码\n",
    "        self.embedding = PositionalEmbedding(\n",
    "            embed_dim=EMBED_DIM, sequence_length=SEQ_LENGTH, vocab_size=VOCAB_SIZE\n",
    "        )\n",
    "        \n",
    "        self.out = layers.Dense(VOCAB_SIZE, activation=\"softmax\")\n",
    "\n",
    "        self.dropout_1 = layers.Dropout(0.3)\n",
    "        self.dropout_2 = layers.Dropout(0.5)\n",
    "        self.supports_masking = True\n",
    "\n",
    "    def call(self, inputs, encoder_outputs, training, mask=None):\n",
    "        \n",
    "        # 获取位置编码，(N,24) --> (N,24,512)\n",
    "        inputs = self.embedding(inputs)\n",
    "        \n",
    "        '''\n",
    "        causal_mask 的 shape:(64,24,24)\n",
    "        64个一模一样，大小为(24, 24)的mask\n",
    "        \n",
    "        [[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]]\n",
    "         \n",
    "        '''\n",
    "        causal_mask = self.get_causal_attention_mask(inputs)\n",
    "        \n",
    "        \n",
    "        '''\n",
    "        mask (64,24) --> padding_mask (64, 24, 1)\n",
    "\n",
    "        padding_mask：64个大小为(24, 1)的mask\n",
    "\n",
    "        [[1][1][1]...[0][0][0][0][0]]\n",
    "\n",
    "        '''\n",
    "        padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32)\n",
    "\n",
    "        '''\n",
    "        mask (64,24) --> combined_mask (64, 1, 24)            \n",
    "        combined_mask：64个大小为(1, 24)的mask\n",
    "        [[1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
    "\n",
    "        '''\n",
    "        combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32)\n",
    "\n",
    "\n",
    "        '''\n",
    "        在combined_mask与causal_mask选择最小值，大小(64, 24, 24)\n",
    "        64个不再一模一样，大小为(24, 24)的mask\n",
    "\n",
    "        [[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
    "\n",
    "        '''\n",
    "\n",
    "        combined_mask = tf.minimum(combined_mask, causal_mask)\n",
    "     \n",
    "            \n",
    "        # 第一个masked self  attention，QKV都是inputs, mask是causal mask，强制训练时只关注输出位置左侧的token，以便模型可以自回归地推断\n",
    "        \n",
    "        attention_output_1 = self.attention_1(\n",
    "            query=inputs,\n",
    "            value=inputs,\n",
    "            key=inputs,\n",
    "            attention_mask=combined_mask,\n",
    "            training=training,\n",
    "        )\n",
    "        out_1 = self.layernorm_1(inputs + attention_output_1)\n",
    "        \n",
    "\n",
    "        \n",
    "        # cross attention，其中K、V来自encoder，Q来自decoder前一个的attention输出，mask是padding mask，用来遮挡25个单词中补白的部分\n",
    "        attention_output_2 = self.attention_2(\n",
    "            query=out_1,\n",
    "            value=encoder_outputs,\n",
    "            key=encoder_outputs,\n",
    "            attention_mask=padding_mask,\n",
    "            training=training,\n",
    "        )\n",
    "        out_2 = self.layernorm_2(out_1 + attention_output_2)\n",
    "\n",
    "        ffn_out = self.ffn_layer_1(out_2)\n",
    "        ffn_out = self.dropout_1(ffn_out, training=training)\n",
    "        ffn_out = self.ffn_layer_2(ffn_out)\n",
    "\n",
    "        ffn_out = self.layernorm_3(ffn_out + out_2, training=training)\n",
    "        ffn_out = self.dropout_2(ffn_out, training=training)\n",
    "        \n",
    "        # 最后输出为VOCAB_SIZE大小的向量，对应位置的大小为概率，可以查索引来获取相应原单词\n",
    "        preds = self.out(ffn_out)\n",
    "        return preds\n",
    "\n",
    "    def get_causal_attention_mask(self, inputs):\n",
    "        '''\n",
    "        causal: 因果关系mask\n",
    "        '''\n",
    "        # (N,24,512)\n",
    "        input_shape = tf.shape(inputs)\n",
    "        # 分别为N，24\n",
    "        batch_size, sequence_length = input_shape[0], input_shape[1]\n",
    "\n",
    "        #范围0~24的列表，变成大小(24, 1)的数组\n",
    "        i = tf.range(sequence_length)[:, tf.newaxis]\n",
    "        #范围0~24的列表\n",
    "        j = tf.range(sequence_length)\n",
    "        mask = tf.cast(i >= j, dtype=\"int32\")\n",
    "        \n",
    "        # 大小为(1, 24, 24)\n",
    "        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\n",
    "        \n",
    "        scale = tf.concat(\n",
    "            [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)],\n",
    "            axis=0,\n",
    "        )\n",
    "        # (1, 24, 24)铺成(64, 24, 24)\n",
    "        result = tf.tile(mask, scale)\n",
    "\n",
    "        return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9131807",
   "metadata": {},
   "source": [
    "**测试一下transformer decoder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1c3967e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试模型\n",
    "decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2)\n",
    "# decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "884af6fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 24, 10000)\n"
     ]
    }
   ],
   "source": [
    "# 测试输入\n",
    "for i in train_dataset:\n",
    "    # 前0~ -1（24）个单词（去尾）\n",
    "    batch_seq_inp = i[1][:,0,:-1]\n",
    "    # print(batch_seq_inp.shape)\n",
    "\n",
    "    # 前1~ 个（24）个单词（掐头），用做ground truth标注\n",
    "    batch_seq_true = i[1][:,0,1:]\n",
    "    # print(batch_seq_true.shape)\n",
    "    \n",
    "    # 将batch_seq_true中的每一个元素和0作对比，返回类似[true,true,false]形式的mask，遇到0，则会变成false，0表示字符串中长度不够25的补白部分（padding）\n",
    "    mask = tf.math.not_equal(batch_seq_true, 0)\n",
    "    # print(mask.shape)\n",
    "    \n",
    "    # 输入decoder预测的序列\n",
    "    batch_seq_pred = decoder(\n",
    "        batch_seq_inp, encoder_test_output, training=False, mask=mask\n",
    "    )\n",
    "    print(batch_seq_pred.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dbfbb76",
   "metadata": {},
   "source": [
    "### 5.构建ImageCaption任务模型 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53d65d9",
   "metadata": {},
   "source": [
    "这里调用到上方CNN、encoder和decoder  为该项目的模型流程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "59dbf637",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageCaptioningModel(keras.Model):\n",
    "    def __init__(\n",
    "        self, cnn_model, encoder, decoder, num_captions_per_image=5, image_aug=None,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.cnn_model = cnn_model\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.loss_tracker = keras.metrics.Mean(name=\"loss\")\n",
    "        self.acc_tracker = keras.metrics.Mean(name=\"accuracy\")\n",
    "        self.num_captions_per_image = num_captions_per_image\n",
    "        self.image_aug = image_aug\n",
    "\n",
    "    def calculate_loss(self, y_true, y_pred, mask):\n",
    "        loss = self.loss(y_true, y_pred)\n",
    "        mask = tf.cast(mask, dtype=loss.dtype)\n",
    "        loss *= mask\n",
    "        return tf.reduce_sum(loss) / tf.reduce_sum(mask)\n",
    "\n",
    "    def calculate_accuracy(self, y_true, y_pred, mask):\n",
    "        accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2))\n",
    "        accuracy = tf.math.logical_and(mask, accuracy)\n",
    "        accuracy = tf.cast(accuracy, dtype=tf.float32)\n",
    "        mask = tf.cast(mask, dtype=tf.float32)\n",
    "        return tf.reduce_sum(accuracy) / tf.reduce_sum(mask)\n",
    "\n",
    "    def _compute_caption_loss_and_acc(self, img_embed, batch_seq, training=True):\n",
    "        '''\n",
    "        计算loss\n",
    "        '''\n",
    "        # 图片的embedding特征输入encoder,得到新的seq，大小（N,100,512）\n",
    "        encoder_out = self.encoder(img_embed, training=training)\n",
    "        \n",
    "        # batch_seq的shape：(64, 25)\n",
    "        # 前24个单词（去尾）\n",
    "        batch_seq_inp = batch_seq[:, :-1]\n",
    "        \n",
    "        # 后24个单词（掐头），用做ground truth标注\n",
    "        batch_seq_true = batch_seq[:, 1:]\n",
    "        \n",
    "        # mask掩码，将batch_seq_true中的每一个元素和0作对比，返回类似[true,true,false]形式的mask，遇到0，则会变成false，0表示字符串中长度不够25的补白部分（padding）\n",
    "        mask = tf.math.not_equal(batch_seq_true, 0)\n",
    "        \n",
    "        # 输入decoder预测的序列\n",
    "        batch_seq_pred = self.decoder(\n",
    "            batch_seq_inp, encoder_out, training=training, mask=mask\n",
    "        )\n",
    "        # 计算loss和acc\n",
    "        loss = self.calculate_loss(batch_seq_true, batch_seq_pred, mask)\n",
    "        acc = self.calculate_accuracy(batch_seq_true, batch_seq_pred, mask)\n",
    "        return loss, acc\n",
    "\n",
    "    def train_step(self, batch_data):\n",
    "        '''\n",
    "        训练步骤\n",
    "        '''\n",
    "        # 获取图片和标注\n",
    "        batch_img, batch_seq = batch_data\n",
    "        # 初始化\n",
    "        batch_loss = 0\n",
    "        batch_acc = 0\n",
    "        # 是否使用数据增强\n",
    "        if self.image_aug:\n",
    "            batch_img = self.image_aug(batch_img)\n",
    "\n",
    "        # 获取图片embedding特征\n",
    "        img_embed = self.cnn_model(batch_img)\n",
    "\n",
    "        # 遍历5个文本标注\n",
    "        for i in range(self.num_captions_per_image):\n",
    "            with tf.GradientTape() as tape:\n",
    "                # 计算loss和acc\n",
    "                # batch_seq的shape：(64, 5, 25)\n",
    "                loss, acc = self._compute_caption_loss_and_acc(\n",
    "                    img_embed, batch_seq[:, i, :], training=True\n",
    "                )\n",
    "\n",
    "                # 更新loss和acc\n",
    "                batch_loss += loss\n",
    "                batch_acc += acc\n",
    "\n",
    "            # 获取所有可训练参数\n",
    "            train_vars = (\n",
    "                self.encoder.trainable_variables + self.decoder.trainable_variables\n",
    "            )\n",
    "\n",
    "            # 获取梯度\n",
    "            grads = tape.gradient(loss, train_vars)\n",
    "\n",
    "            # 更新参数\n",
    "            self.optimizer.apply_gradients(zip(grads, train_vars))\n",
    "\n",
    "        # 更新\n",
    "        batch_acc /= float(self.num_captions_per_image)\n",
    "        self.loss_tracker.update_state(batch_loss)\n",
    "        self.acc_tracker.update_state(batch_acc)\n",
    "\n",
    "        return {\"loss\": self.loss_tracker.result(), \"acc\": self.acc_tracker.result()}\n",
    "\n",
    "    def test_step(self, batch_data):\n",
    "        batch_img, batch_seq = batch_data\n",
    "        batch_loss = 0\n",
    "        batch_acc = 0\n",
    "\n",
    "        # 获取图片embedding特征\n",
    "        img_embed = self.cnn_model(batch_img)\n",
    "\n",
    "        # 遍历5个文本标注\n",
    "        for i in range(self.num_captions_per_image):\n",
    "            \n",
    "            loss, acc = self._compute_caption_loss_and_acc(\n",
    "                img_embed, batch_seq[:, i, :], training=False\n",
    "            )\n",
    "\n",
    "            batch_loss += loss\n",
    "            batch_acc += acc\n",
    "\n",
    "        batch_acc /= float(self.num_captions_per_image)\n",
    "\n",
    "        self.loss_tracker.update_state(batch_loss)\n",
    "        self.acc_tracker.update_state(batch_acc)\n",
    "\n",
    "        return {\"loss\": self.loss_tracker.result(), \"acc\": self.acc_tracker.result()}\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [self.loss_tracker, self.acc_tracker]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a996e047",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa8a7c4a",
   "metadata": {},
   "source": [
    "## 编译模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21610a90",
   "metadata": {},
   "source": [
    "### 模型实例化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a0e89c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model = get_cnn_model()\n",
    "encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1)\n",
    "decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2)\n",
    "caption_model = ImageCaptioningModel(\n",
    "    cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0ba76f",
   "metadata": {},
   "source": [
    "loss、早停"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb3b84d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss\n",
    "cross_entropy = keras.losses.SparseCategoricalCrossentropy(\n",
    "    from_logits=False, reduction=\"none\"\n",
    ")\n",
    "\n",
    "# 提前终止\n",
    "early_stopping = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n",
    "\n",
    "\n",
    "\n",
    "class LRSchedule(keras.optimizers.schedules.LearningRateSchedule):\n",
    "    def __init__(self, post_warmup_learning_rate, warmup_steps):\n",
    "        super().__init__()\n",
    "        self.post_warmup_learning_rate = post_warmup_learning_rate\n",
    "        self.warmup_steps = warmup_steps\n",
    "\n",
    "    def __call__(self, step):\n",
    "        global_step = tf.cast(step, tf.float32)\n",
    "        warmup_steps = tf.cast(self.warmup_steps, tf.float32)\n",
    "        warmup_progress = global_step / warmup_steps\n",
    "        warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress\n",
    "        return tf.cond(\n",
    "            global_step < warmup_steps,\n",
    "            lambda: warmup_learning_rate,\n",
    "            lambda: self.post_warmup_learning_rate,\n",
    "        )\n",
    "\n",
    "# LR调节\n",
    "num_train_steps = len(train_dataset) * EPOCHS\n",
    "num_warmup_steps = num_train_steps // 15\n",
    "lr_schedule = LRSchedule(post_warmup_learning_rate=1e-4, warmup_steps=num_warmup_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533ae29e",
   "metadata": {},
   "source": [
    "### 编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b56c0f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "caption_model.compile(optimizer=keras.optimizers.Adam(lr_schedule), loss=cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ddb841",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd143136",
   "metadata": {},
   "outputs": [],
   "source": [
    "# caption_model.fit(\n",
    "#     train_dataset,\n",
    "#     epochs=EPOCHS,\n",
    "#     validation_data=valid_dataset,\n",
    "#     callbacks=[early_stopping],\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff8abe7",
   "metadata": {},
   "source": [
    "### 保存权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5225c9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# caption_model.save_weights(\"./my_model/checkpoint\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610b5706",
   "metadata": {},
   "source": [
    "### 加载权重进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "09b5b48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_status = caption_model.load_weights(\"./my_model/checkpoint\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "38065c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = vectorization.get_vocabulary()\n",
    "index_lookup = dict(zip(range(len(vocab)), vocab))\n",
    "max_decoded_sentence_length = SEQ_LENGTH - 1\n",
    "valid_images = list(valid_data.keys())\n",
    "valid_caption = list(valid_data.values())\n",
    "valid_len = len(valid_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315b388a",
   "metadata": {},
   "source": [
    "+ 获取CNN特征--》\n",
    "+ 传给encoder--》\n",
    "\n",
    "+ 1.先提供<start<start>>--》\n",
    "+ 2.传给decoder推理--》\n",
    "+ 3.不断投喂给模型，直到遇到<end>停止.\n",
    "+ 4.如果循环次数超出句子长度，也停止."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ff37b6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_caption():\n",
    "    # 在测试集中随机取一张图片\n",
    "    random_index = random.randrange(0,valid_len)\n",
    "    sample_img = valid_images[random_index]\n",
    "    sample_caption = valid_caption[random_index][0]\n",
    "    # 读取图片\n",
    "    sample_img = decode_and_resize(sample_img)\n",
    "    img_show = sample_img.numpy().clip(0, 255).astype(np.uint8)\n",
    "\n",
    "    plt.imshow(img_show)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "    # 保存\n",
    "    cv2.imwrite('./img/raw.jpg',cv2.cvtColor(img_show,cv2.COLOR_RGB2BGR))\n",
    "    # 获取CNN特征\n",
    "    img = tf.expand_dims(sample_img, 0)\n",
    "    img = caption_model.cnn_model(img)\n",
    "\n",
    "    # 传给encoder\n",
    "    encoded_img = caption_model.encoder(img, training=False)\n",
    "    \n",
    "    \n",
    "    # 1.先提供\"<start> \"\n",
    "    # 2.传给decoder推理，\n",
    "    # 3.不断投喂给模型，直到遇到<end>停止\n",
    "    # 4.如果循环次数超出句子长度，也停止\n",
    "    decoded_caption = \"<start> \"\n",
    "    for i in range(max_decoded_sentence_length): # 24\n",
    "        tokenized_caption = vectorization([decoded_caption])[:, :-1]\n",
    "        mask = tf.math.not_equal(tokenized_caption, 0)\n",
    "\n",
    "        # 预测\n",
    "        predictions = caption_model.decoder(\n",
    "            tokenized_caption, encoded_img, training=False, mask=mask\n",
    "        )\n",
    "        sampled_token_index = np.argmax(predictions[0, i, :])\n",
    "        sampled_token = index_lookup[sampled_token_index]\n",
    "        if sampled_token == \" <end>\":\n",
    "            break\n",
    "        decoded_caption += \" \" + sampled_token\n",
    "\n",
    "    decoded_caption = decoded_caption.replace(\"<start> \", \"\")\n",
    "    decoded_caption = decoded_caption.replace(\" <end>\", \"\").strip()\n",
    "    \n",
    "    sample_caption = sample_caption.replace(\"<start> \", \"\")\n",
    "    sample_caption = sample_caption.replace(\" <end>\", \"\").strip()\n",
    "    \n",
    "    \n",
    "    print(\"预测: \", decoded_caption)\n",
    "    print('真实：',sample_caption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d61c13c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  土地 上 有 两个 穿着 深色 衣服 的 男人 在 给 一个 穿着 深色 衣服 的 男人 递 东西\n",
      "真实： 一群 满身 是 泥 的 人 在 柔软 的 泥潭 里 嬉戏\n"
     ]
    }
   ],
   "source": [
    "generate_caption()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a21e100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  一个 右手 插 在 口袋 里 的 男人 站 在 舞台 上\n",
      "真实： 舞台 上 有 一个 左手 插 兜 、 右手 拿 着 话筒 的 男人 在 讲话\n"
     ]
    }
   ],
   "source": [
    "generate_caption()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c46d2229",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_imgs(path):\n",
    "    input_img = decode_and_resize(path).numpy().clip(0, 255).astype(np.uint8)\n",
    "    \n",
    "    plt.imshow(input_img)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "    # 获取CNN特征\n",
    "    img = tf.expand_dims(input_img, 0)\n",
    "    img = caption_model.cnn_model(img)\n",
    "    # 传给encoder\n",
    "    encoded_img = caption_model.encoder(img, training=False)\n",
    "\n",
    "\n",
    "    # 1.先提供\"<start> \"\n",
    "    # 2.传给decoder推理，\n",
    "    # 3.不断投喂给模型，直到遇到<end>停止\n",
    "    # 4.如果循环次数超出句子长度，也停止\n",
    "    decoded_caption = \"<start> \"\n",
    "    for i in range(max_decoded_sentence_length): # 24\n",
    "        tokenized_caption = vectorization([decoded_caption])[:, :-1]\n",
    "        mask = tf.math.not_equal(tokenized_caption, 0)\n",
    "\n",
    "        # 预测\n",
    "        predictions = caption_model.decoder(\n",
    "            tokenized_caption, encoded_img, training=False, mask=mask\n",
    "        )\n",
    "        sampled_token_index = np.argmax(predictions[0, i, :])\n",
    "        sampled_token = index_lookup[sampled_token_index]\n",
    "        if sampled_token == \" <end>\":\n",
    "            break\n",
    "        decoded_caption += \" \" + sampled_token\n",
    "    decoded_caption = decoded_caption.replace(\"<start> \", \"\")\n",
    "    decoded_caption = decoded_caption.replace(\" <end>\", \"\").strip()\n",
    "\n",
    "    print(\"预测: \", decoded_caption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d19fab1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  一个 穿着 黑色 衣服 的 女人 走 在 平坦 的 运动场 上\n"
     ]
    }
   ],
   "source": [
    "path = './a01.jpg'\n",
    "predict_imgs(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bd051da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ESZqAKcfuAOP5rc0tqXolo5uxTovNDZ666qDWujJpTiF/Vhw0yRi4QRMCQjQGUY4oY6l8G5KNaMSYDYbMwI3xpeT+JIzyUgiDzIk2EepUovy0g1bggKiwTEZg7sC8jG9+8xYuX9qDbNPJAyDzSLXsnxKOak9xlhUURSWQJIS8UZUREZkRir2is7lzW7q1yWzm1e2+CofMLOfMol7eWluElN25cvHEdmytXOZWqt+Bz18Mp4NbZmry7NmEaeJMFcf8dfI73OHjte+FMpsIFsKCDGOaspOnpxguibZmqZvRtEJCwhe5VTuk7B+hfFnuv/DCO71ftqB29kfPaAembJiuDDgwOYzrHr75/FXs7wWQk310pufbFLMqmyEjNhSlTxY+YM0OEkI1TiWS2B+4etx5r4ouRzBHRBbpJPat6RLC6Uh98MlR18FoVol+WG/i7TRJZhckyM6I6WckCVgqSExpTyvpZ7k3dkCiUnZRIK1orEyRYbCLGXb2vb5KFS7BCDFrQK05TL+z4Vnc8LCQTT2wJlaUnVmfoEREjggOHimuqEImd1MIEkbSrCwCUA45mDvHlsNsUCVUTrOnRKBETk0Rk7aF1AVpPHv+7A8gzrbeLIK0hyuXJ/jGN66habzo786SErrxrPzeOZcAqjwJlpDgTAIX6pxbxBVbTXfGsNkEhtDieEoVGaJp+XqNXT9/6oJ8lMn+rrWkxc+LaLbHYW/aardmtFiCA6D0LhgTyRX5yYcbU2SJpEbKqp68nHyfXg6s27dQMoUZz5KtaawJ5bMmd5DNWg5RHtKwuCG9TtfEj1Pp5YkA9tjfZ3DsAVzp/R6ccCLjAyj7k60vIoZnJ+mlil/GiCKAFE833OuaTASBlmN4Ahw5OJOyTgjWdM15bbFDCCXB2YIRmAd45ZVNvP7GLiL3APLitdDZlvRpKo9zVZqAp7yFjMxDpuVNxJTJEuNQy8YyTk5OCnFUyDa3rFYY8cp3OaY6t1tPYO9w97PfVMq0OG5bNUxqtv2bUr31Lkcg7wHvNC/Zdjzcgd5IRnSGoDZa883n35Nizou5v/gV5hgIB8T5ZvcGTGIAJ2eUrnCx3Y/ACAG4+MYudraiEAYkf9bEndiAJh1VdTKNj5DEgwkJYYaZaUWijBemxbREmflYhBhhdJDm/hbV2jZpWG5tBLkemJfx4re2cOtWDUcenrw+KAPJ3pshbuoPNN5pjL7czpTZwAGopSqp7WZlUw0BfYayEg0u56JR0UbUnWzncwnuu0iaqu4YczD167Atq9k2KpmJt6QHoIUmi4aeVX9ju6ZsG3FzilO69FuZvJm1nZkPSusjHyQTZ7b6e5g2a25sz9Hxr672sbQWUTc1rlzewUsv30LTkEQGuLxR/RslqiKmYmHm8LKZlpLRqbYH1RpVOZBuWvdkGmKrp6S+mUUJ79YWE2dLQy2IhwjklnBzs8K3nt/C/i6DWdzHnQIOeaBatMs54TjOyeLGZMIUxJBiM/MnwDq+tNM8AcV+tTG342vKXjLnVzycok2b55SH9621wpcquoHavneW3kY5AQDFeOlwquK0xc5p2aYzngqpnRwdyBrJHClYGkZU6Nl3IjW7/aZqjLbNixgnTqzgkXecQr8fUE8Yr72yhQuv7yFEFEyBW+sqdr94zp0jJdB2OKRbRiXd60gIzhVwK2DTElaq4ZWaxCIILA6lJBUwcwKRQJljXnhjD+fPb+ZKa6aOUtE9QQmOc/qTSktWoCJ5ew/XklPVnGb6fWxNi5IEBDIj54yKqtF0HT+dtLhDjum2GtloWRl5nJGON/1d2cQt39090W5Zo+BkM7X7cXOdXe14XBmXbSOVaSslpDKMbT+prvmCjQyHbbYZH+xSdQdSTcR7wkMPHcHp0ysgF7C/1+D8yzexv9ukUaGwqVu4YZsBVF+1GGdLYnMbh8pdRw6kxQEUagmurBJWTDemnF/PM21xaQc6hNCahtfkARmwcwTmPl781hauXt5X6WlJRxnJs0fRUvGUiEniqFC1wPbYHdZcykDLALRdlAQUmT9UaFhJ0ZiyOm2BOuFWvGVsOmAWPPef2jk0/ZLtZuX6zOraAuX5khYREae+0mbgYlwgj1YMNVNrasbA59vvkrccwWmd8r23C1d9VpAN0o5coTHJTs3ADQZDwqOPncCx430wAq5d28fLL21jMunnkAYASysl9dpGOK0xpAAjKf1q6rgIaWrnuTAnHDRnnAixYg3JMoPUi2POz0LtnQ25RaAgjWWqo0HU2Uq9TaxxG4fxuIdvPHcd21uNbNGBcccIIkhWEJs57WFeUMktLNz9SSM4mDqzmV68E+rSEMCMiZcq6qxMohRnzbFF0xvuPnm2c4azFm/I7hKcZr4AcAGDWeCa7appkxFBHCP2/MIFhfyUJB46cJE3TDSTcAGkHRnZdpih+h5SxTUREUOEg4ODT8QhdnIOnZ08NcQj7ziKwZBQT4BzL2xjZ7tK+GwzYcUZIUIHdpy0DZsxK7Flc0FgY2p1IR+TRsnMecpsc8xqr3QvDrZ5bbHkLPRnA6LqgToYFXfkcfNmxPlXthCD6uvUdh/Y4AUNRL83+kjhDhaQH7a1UbsEUZaOLYxJDiNBpEK5TddxsWilkjal6r7Fl1ED6biEm/ssxbre2u4/ZYw0JfEK+BiiqCrFnPfX2hgYSNrQLAi346wlrMryJ/ldOT8jplJiph7eQty4tR+ASibksLMzwmh/DN8j3Hv/Ok6dXgZRwHjECHWV8MK0pLxJW/oVaUzCVJQghQkkET2fybQ+ZToxgy1BgTODXMSSDvDWGnfPXI/YNHwN8qqrKsYeXn55G6+/tg3z0rRLlShYIiMGA6q+KNtN1J7GgS1PkhMQnEE7lZgomn1dPMBU2Va/VOy7xWIg3lkzyaSzTQnSNP1aSKDzHUDmJxBmk5QvtCHSfkbr/lb/0zCYkqJzfu8+8q1pIdlkSXWMkx1d4dr1Lbz80iU0dcRwucLjT5zCiZM9OB9AyOX8GUnIAarOgkPSVuQyiyyUDrsC40ocsm9L6YhCABUX2e+5XMzsdggxpS7j0kIjmxkLl4cDMyE0fXzz+ZvY3ByLFNAmU1TlVbkVM0thL5fJ6k4pIIGLjFOpysFdNAQKOih+oDb0tDGQs1jeNrPzrmHt/JbmPK3mvnWmczhNJzmS7nyZVYXMzkSRbIyUSgcgNMD581dx9coewMDJkwM8+s7jGA4nANUyYtVFswCM4MAARymAYCxM7U3Tt3Qi5aTSwBKd63dlbm5bvxCbs/Iezrc3hnTbATZnRloyUSej1l0yRlROHQse+7uEF751C5NJhPc92LaepODqJjxxNBIoenirifuWkFOJ3BwcMEvEta5J82qLTnl8R0KYHWZqTgDyixVRyL2Fl0ckrzakJcOHt6TyzYaMIEWETy/djrz4Ro0Ro0DPsiaQKLeq8pPBgoqrSe5nLd5MUV6u9DCLsyUWqYMz5285lhnjxZkI1pwTSz73GO06vHr+OkajMeAmOH1mGafOLAFUgzmKn8v8HCyw8ZAdKb0K7UoV+kDDYVLhYjiiUUDAAdFlO9PZhgXihF95MUjVZTUB57SDbU5VFJMakMBOGfGJpJIBCIF7eONCjVdf20WIpT1EiVBlfGYU341QRVawshpOacBJ0BcqR47Zlb20f8uESsUVlK5LrhKiO3pxUn/MMTAdLnnrkJF/U71msXFgD9ayFGiLD/u+lBJtW17lXWFLm6ZU2qVp3RY0C2OI0y6HaUCWlOIQ4wA3rjXY3hmBeYLhSsB3PHkKw2EDAKh8Xt9c2M3laoUoXzYsSkTZYe35veGz3ZZwrIwW2KWUhMG8dkCcs0h/Q36PTk5oljQEsENTe5x7YRNbWxMV3cp9CpEk9pCpCh0j8C63jlto+sfDsAfDm5Y9+NZG3YLgDAfa3WzF6ildHHbw1HlL0/AsPJGZFH3hRbDWgXP6aASvr7njUmpm2wmVGX3b7vBA7MEqdjkKOHa8j/5AxldZTSOTXlAiJQDMFmBJMe4EKyUmzfGBg+3cZOM1nRQcU73zWDuAmzdRAIfdlVI6HSiLfRSGshCbA3sHoh72dvt47pmrGI9NWiEDsGA1JtUOjyy31w7X5TSjKX9LcqdDoIlQgTt6ofOeD9y7+tZay8E0x4nUbm0yzO6kMohfaCswHDbkd+luKv5vjWkKEovGlCWbkXMEgdmBqBJ5qsQA7wEnGzNicg5qlMCVG+47jh2WtP4kQFhytUsHnBCpJcRn0VWyVoGNy7YRGEBQWZDnuih185BsmotlsY7VUM56iejRWrSLXA9XrzJeObeNppEk5cjcWe52O7yP9uBWokvLXj7gntbnroeOCwSccf0dj7FQdcv0r7vZZnqAk4q5aIAdEqRsV7V9x9NzaXcEZPnd/plm/Js3oMzIpFD0eAxMalITyuvuEYmDIlSIwSOGIWLwAPvEaDkSmL1IeK7AXIFQpTIv7XGnpyIr7G1MZs5zKilEko2yMyn3eDBzXFzgK+n20olTuUxWw5JFHcg2WFTNQw7NaUIf51/exYkTQxw/1QNiA6v9IgCmtM/S2eDvEmKWRxyQ5kslZoI2rSZ1bJ69Z9qDZTopB593zECZ8kc4eE6RreTl20OYt9NyPds2Q9Jj31T5iZkpwzKQVAVMSNoxVdRms+/KzQ5Tz5zXlCk0CIjEGE+Ar/7ZVQyHEQ8/dBwnzyxjOOxjOPSYTCY4//IWrlwWQibymEwqwAGbWwEvfmsTVd8OzSXsbfeUYQGSfRUL061I51AVmu24bFimnM4pcjrxWdTtnJGVNl+wHlt4wHQPIM7YovT0vabk6UUZqIS0SFLk2WFvv4dnn72OD3/0NFaWKsi21aBF3ktf2N1vJXrIqVYZGVq8sSQsm0bRT4vwIItyN0dcKFh3VXt4u5pkwkSYo1BadvC1PBJtEanXZWI8tAOMkixCCA3ggCY4vHmhgUPA1uYVfNfyfTh79igeeUeDF164gVdemYDYDtiFSkiP7U3G1756C0RSoNv5CjFIZttCZkp2IGWRM+VsDkITpPghsVKNdaZzOmPB7LhjJ0+3Q+TW5s286UToJEHtmvw8pMErp6Uert0Azp27hRBtm43A2msKlrtDhCwTuecliKdUt/ma0tRHY2oyzTLdWz/PSkK3BZrf9YzxJ9khnsI7gEOZrH/n93aVNGumv2Yun8nOta4XJMy0aJrIW60ikaFfrEPKKSZwdGiCw9Uru3j5lSsAgIceOYZjx/pgNAhMCMEjBo8QtOoEHGLsIcYBAg9QN5WcOkAoUvuMSE3a+ZZCm3/PsDJ8cSyVPazSQttOLe9QO3hOOyBDSHVjLmrLCmvRAZo0Sp9QvmHlDoQ+Xn91hDff3E1XZqdEaYncfrN0QG4tI9I72VRLaBXTIgGMS3dnS8I0Mi6uBUPqHKljoROWBoqn3i6RZOl+50g8O4f2sPe17SYbU5xKvMiseJZtKN9nU6ILE26VPTn8+Gw8Io0YkUgqszMDFNEfTOCqfUSM8NK5V3D+9TexvOrx2BMnsLwSAeyAsYeAPQTeQ8PbaHgHqPZBfgxXjeD8GEQNvMuVC4Xx2ziM69jsizF21sypJlmeO80lzLLBfmAo5YAjAAvaJdZylaaImjMhFgimihk7LcMgNiWxw2jUxzee3cLqaoUjR/V48LsS08vOqmllk4trylGKBhB1ASyGZSZR8vjJh5aua6pnftps8N4es6HWnztqHRPvsM+dlTNECfmz5Ei/znyGMC2Y3cVU9Fr0b7HF2xhw5pFqsxJpKUyH4dDjA995CvABTWgATDAYVnA+4sy9q3gfn8X21hhgL2sdIuqgR0dEIOgBWhwJ168E7G4HCR+qo0yqflhlP7Mx26tORFKcDtKXbQmzzQDmjMwZUpQmJVrofPl4QFHpwi4oH4CswiQJxRJKkXskoCsn7pn7u8L2jmQPfejDp+ArIOrEOwpiWowul6Xu/4wk0ck8wVSQIJuDivTacl4S9M+baHO/JdcjC25HG5Nt+eHOwAspon0dRhKmp74FR9Cd2qldOdAixdseD+W/Uwg3nZIgeawHk2ceD8PCF3LqgEN/QLj3/iX0hpKd5FUXIgoARTz8yFE59CjmtYhBQyfMiFZXihl/9sVL2NkqMpdcxptsNpTqbjm7bKsaDTsQQnHmbGL8LZxw6GJ/2RafbD1lzNtojKsK4UnObP5sBEGcpRpY4p+XLu7g9deu4eF3nEjivux/9oKVkq8LmA6dFB3MUjLLObWcXNqPXDOLsISAzOnRTloudeDZz5rV7pwQDv+Mw95L6T95c6eSf5Y2kdRDNQuI3CFrApajURuTWYhTc2rtc4i5ViP1GBwjmsDg6ADn4Mh2Qqnl5ygvOBMIQX5XAjPvPGDq/zTzlTlFpDh9mqtqJGYbk3zT0ijYep7fDpScLRAlIdMGFoi0shhBYk3qVqciRKBH+zXB4datXcR4DCUlWSglAq21zeQ4f0MvQyocdQO6cQ5Av93+gpuFH5iBqJvrnZkMhzVtKFkYqZSoGichBFx47Qq2NyOOH9vAvfdv4PqVMTa39uG9w/Hjq1hdqzAeN3jzwja891hZG2B1Ywh2DYjlXJq0B7NQAgpPhLQZw8173i3eq+aSapOSIhhSP1Z28yCMPOAgI0b7/JE8atbSBXLuidpprfHHFAM1vsMEcPQ6aVMpYkHsxoHUgWMTJSNNl9YyJj3VOJT12ZYGZZx20Tzl/nauZsnpsnNrVl//9yT8yEkJxaLdEwc3dQqBEVkKbTor+MyFSV94jtt3q+bkoLn46ufQ0AXpgbnPPXsBjzzC2NhYxVe+/Aa2tmo4B7zj0RN491Mn8eK3LuPZZy5jMBji6Ikh3v+hBzBcNXUp/yH1TouJKScCMecN+LPqC6uNl3DVYuFZ6c0nCwEQb+4BFHrQtoQcdHft7w3YBjjLLwSyEprS9QEQBXW+61krdo2RMlsiuD6XOP+unsPsPWs/P+VFZiUaBVrBDPzF0pOzfZB2EXSV7Nk2x2wF+v8OzeB8F7oBkEyDstdCSsF190iWawwgEQjDRd2UyAznHc6cPQFPy3BU4cbNEW5cb0DoITQVLr+5h52dGq+8fBUcltE0Hpcv7eDK5ZuaWEKyzxh5bUX2sGmlM8N9UemDCq2NwbrjpwwjtVIuslk4w+FWtkOwQ/GGlBXD9JhRSIFdeZgjnyKW8lyncVGdsKowpZpqHDlJp1lYQCTcq8w7JQY7KS0RCXJsA0WwC2AX5ZVOLc6EPDWzFB7Rq5jAAYiRhFnwYQszLwbyX5Z2ZzHHg9PM5j4vMXf9TKw1XDORRqD1nZTCkVc+xUn3EnNMtaYo2u5DYehUiUnlHFBPIirfw733LWF5yaGuG8QY0NQBa+tLOHN2HRwYzaSGlg1I+3YBl30OLGQmhx0pW1BvrLmOogoT0nkwYkpAkHCUVs83Rk/FfLMYm9kOlJwA0snE3SUyLtZajlRWjLQCNxe/AnJUAilHymRaZmhS8Uv32tzRrMzOGUjEh8tCKZmG3KeInJDrMNL3/xrtsAR6mPzPxQ8SU4EzasJ8F+VOjmJgySGDJImidQUrlJWORbQ8X46IUWoTEwExMMhHnDjVR28QtT9GiBMcOdbDqVNLwvBZNEJONiyQR2Tlboxo21pZwlQTHkygKCq2s2ohRtBRiJS09rPsa6U2vs1oi721juHYzpoQSDq01RLbocIUIaUKhbWZpl3afJb2N0XkhUqavtPniNM8AyPZr8gOqlmtHYecj4zM3CI4BsTjoEyb4BGjbYCe98D/6xBsd76L2ltlRCJUpIJdUKmX8ksjmYFn5r2OLyYno1kXpdaT7DkgFZgDAYi12opazT0EWGVH52QjNZNslXeOVXiQJpdIfrNDhWzbFLMgpCQpwQILqpGqqDZQlqMg7DqSqoOR2hvIRbZYfvZ8+C321lJO17PDc0quEWFxQlNR7dGiHhRLlN6FVKN22qJDuRA66iTauXskz/TdU2ls3XgUd39j+2OwzT2q3iKA1a8OLXHsEW8/0eY9hwr1QzxzXsL+29EMJcqkd/lBLTBuXdlG1iTETIrpx8LWs+FHPbFO1pSzFEaUipBOmTozmsBaRJpaRJi8IAVAOT2xyH8ipP4BSmelxOQkLaagAk0qZ2SzD0xaveEOJWeCD7OeRWHqhARPk0TUV8kB85RskkpmRbLCzFbQnJC/h5FsdhIVY+vezEL8s1QGTv8h67ElR6QcypHvgoz3oPqw3WcAb9G7eZstxZ0XtzYhLtIE7k4zerQ9j+mJBqSoEhJ5DZjsP2WehnKaDEJwIOcBVyEAiDGigu2lFJg7isqoHAiVCBUmgHvwziE0mk5IrHhpFiTJc6CEBpPUlKIWpjl2Zpn+n664qlKWFIt1DnAEDoRFovPALWMw0Z/UScAcO4mIlGlZ0Wb5PmoChCm3BggFMIkkmmnT2BYqprRODAuf2EH3sxArczLDgjR1NvAXNiRTAWqZq2tB12lQ7TAh89Ji+QskTGCqmmDZWkvf0hzeHtLM8M5Ia2ZL2qJNGu40JphUQSqxLA80KZM6Zu/B5BA5ogGrMlrgCkuqqfcVmPuypix1C5zvaa8BjljS89TcsuACm5OSxBmUBA7ZGGJihimAyHKatvAbKiSrMRVq04zd7eavwkLibFwDH2Uy0oek5YGQ93Hao8jJLk4WtYJgCQpZS8+LV4irGS2rL/a/RoqokMAz5ySx0G4P9lmAQ62MohRrsiJjyMzE8iJThKfzYO7qYKS7cd5miTTVaJHPb46MVKS76yPtxCoziFStSkyzwB+tp5vNomLgqZ+SQGWNnHPwRGJbgmBnszZNjcjApYtjTMYe/b4lADjsbkcwywnWUjWe9JDmXN/IUcYYLhJuvA2JXdauOLF6JLwGACcxXTuS0rAyKH7EqLSxYOEWEmccBvDIoc89iLMHkBqfMFxMUDSpZOwhg1LuQRLtJt4diMNM7IhpomYTqPGMrmo23dItRUvIScYw0tX5vlLKgrSqBWM2as8eQ8mA/qK8upREIM/fFtdpiWW9TZrtIkZhTj2wIKpzDkG9r94pCnMnLgjoGbCcN7wzw7sqfd8EuSdGgq8I4BrnX9kCo8Jg6FB5h16vwqXLt+CvEZx38FVP8UpDHVx4440AW8Qn42mznzQxMMlZKUwARdfGKDKdyhW+i8XAX6h/rZ1dBnqNuoELQCHbkpaHmOVVVl2gv5XczqRQub2INR5UKJwoSi/NAsWclhSn9OqCgIqXJTPM0vvlF+G2LIqMqE4MzC7fqJ/VyZCdU4cb+UHNEDIW/UpMUGHsHCpyiTlFsMbZdH6lEwh3nyZbMdT0zFg8q7MKlBVd+UadPF2N1npgu48UBoTRqEE9AeAq1KEGYwLngJMnNrC+7lE3O4hxF6fOLGNpuYezZ4/AUUAMjLW1AY4dWwWghzfrHIjyxgfD01QSExanz6psV6MyZ1F33W3bGenZsQBShtO8tlBybty7gjCuMX6zQcU+DS0Bs2U054GKzacqHovHzH50ZAAGLEPCiIRKzqXAYpKyJvlZC9CKSqJAQoD2NUX/9gxkLbtQ1JPtkKxJcgA3qX8DuA6286TDccfDN07jFYHO4Njo5mD91RI1isSKFgQsjHGXRjR7lCgjk5m1dsqjp7Myy7DJFBPJeretK5OcJEZosLvb4OVzN/HEk8exvjrABz/yMNbXl7G+McQHPvgQbt3ag3MO99x7BL0e4bHvOIOlpSFCAxw/uYy1jT6I6sxQYQRISfgkb4PLmE/Muh1SCZoZcGq7wrZJFloUTAgBIAfHHjHtIp7PvBfv51wJ2HhoBdfHt1DfcPDcb+njlICb0YBh/q0Uwu0gg0E7v2em2ceSK8WIHecOgVUFQdwuBnYGmjgdsQa9Z7AFoeBk8Mt9rCVagLtPBgKQvd0dXLl0CdevX8PK8hqWlpdw4Y0LYEQ88MCDOHniJJaWV1As1kzl/O1qclJXez3l0OKsqLUkJhR+ibHOGqkQLhPjoQdPYvPqJiaTHp5/7jqWlnt46JENPPzIEqzK3pn7VnD63pWslVHAkWM9rB85lRw3SaRw+Yio8e3OUGbwWuMlSf6r1CSYOtxl+doRkajNCAvXZCFxBmrQW62wce8Kbu7sg8fqtDbuDJMuhQYCRmQHhwikZPgS8HnRRDJCY1Bzhtn2xhzQTK9HDmAfdLt6o7v0bPaz9YEYk6snXcdZNnRHwZCMkbRBYMYokhpYZF919w6md+rIapoaL37reTz77Next72DjY2jGI1GuH79OkITcOmRC3jooQfxHe96EqsbGyB2mmKGFBs8TNhlUWtrGxlrJVtHI4Ks2TpJ32pbUBmGlhGUP89upmUBDz58Alcu13j15V2MRz2c+9YmTp5cxvHjfTA0pkh2yIdDDIJnRFpChBheNUGiCk0TQfCw0Fnpl2Dk4xtaQ2FK4R3DndLSscJ1KkuzRkaKG6lW/h2qtQGAcxErJ/qoNxn7FybgwCD0ZPKWaqWjs1IeZmfGFHJR6LeiszYbE+3zy9IjAeAgpKLWu1kKw5RJk8ZXXJPihgUKKuOZ6pc796naM/X7nKFHsFas63DXRJwqcZhRT2o8/9yz+PIXvog333wDHAN2t7bx5puXQJ4w6PXxxmseo/E+Yox415NPYnVtHeyqZIveteqGinkpBGzFq8qzCmAkRTOfO3Msc3m0OZIY1YDw6BPHcevWPjZvANevAs9/fRfHj+/CeYdAHlXfwTmgqgjOCU56n1MHI0d4YrjKYX8vYDQ2nc8GYfazfJxbjIy5KDeiGl4Rv00aohEri2SnRDPzYXxAEoLYfLHnsHbfEib7IzTXJ6DoZMuPIrFx0LS3XU9rsmoDaYw6TudyOCQ7f7pPfmttLmFy8Wkh4diPUQErYRpO93GbqLn9zBSV0zM82slJpSNE4dda+CxlHMsBsaGu8fK5b+HLX/gcbly/hmYyAThge/MGtm/dwtHjx7C2vIxQT7B57Tpe5hfRNA0ee+IJbBw9AV9VSQu5nRS++Y0RWBkyLHiPsqTUgdZFO8Wy+L7Uyma2iOMnl/D4u07g6S9fwmi3h28+t4MQJjIOB8AHOIqwDRiAlME0rU/s9QZEFbzrITbKRgwnlRiTdtBFKCU22Zca9WdnHFDgQu05tHbWUKHOz2mLc2slXRbsAb/GWL9/GTf2thH3ArxJOhsEW/xQeYiqYaTlHgQmssvDuSIbn2Z7rBKyLgjSzmuLCVs9gmxZR/MAlBQRuBhhti9xVq8yErbDMHZt2Q9RlsgtQiyye5K8LNRlB0LT1Dh//iV85UtfxM0b1xFDjX6vh/Gowf5oD7GpEZqJOiciQj3B7tYmLr72GgDGg+94FCdOnEDVGyIqwhyshSxoqtYZAEoCS5r6FByLKYMTcs9TsecyEGbE0ICIcN+9a9i6vodvPbcN8Cq8X1Fm6ADXqNYhZ54ys5w+pSNwBCkiRLpdEVHqXuluJqdrYM6d1niSNJQYK8e22pswh82EYDWz8kWpxt8CJrmYOA3RmBFcwNLxAdbvY9w4PwKaCv2YPYUWvA1sbqGYdhGU1gan2KEAqMDNGa1Q6A/RKP1XfJ7ZKyfcmntN4t5QaZOBnDo3gDPDOb2mI5Bbe0NVctkzZbcCWie7S3fRHK5gRGzeuIHnnnkGly6+gWa8j6aeIISIpq4xmYwxaca49OYF7O/v4diRdVRVD+P9fURmNDFia3cP73ry3bjnvvvfCklOQRFACtJzSzU3cr3zp82XmozIAcSEauDw2HecxPbOCG9euAnmgV5jmhlpAedivArXqAkHMsYgxEmAHUZc7sNsjUv7tfeRiwUvFpGt0LSGkwQ1LASpzH2RTotDqLXySEWTKmL57BJ2bjEmNxp49hoDKpFOORc47bXL3sxMmGSA6sx+Vj3YZPmla8txZaDIGnQmTNMoUsIyW3ed1gq52OQKjq/zSbOKRmVZYhbCJT0nBf7TICKQjs6xa7MpwBxx+fIlXHjjAvb29xEnI9RNjbpuhDjHY4zH+9je2gHIoao8ql6F/niCSMCorrE7HuPkqVO49777QGl9bq9xOREDBfJmgASt1txmN7G33FS8+EBVuzBJxHnDWFohvPs9J3Hi5A1MaofQTJf1bBpGaCJCjOAgjC9wlJTTYEcPMmKIWF4d5owjM9kKxmMSNIHB/C7myKMCPfSvMQTXVo0WCgfgwNKYBkpWpy+DBgHrZ5dwY38LcTcAXMElFVeTj5lTUSCndlo7hKWIGxjskTy+GfSzc1nL+3M2Upu8pvCCefq7GQQ7dUnnfTsaOs1BjSjFO1ksZFbg01jJJlMQedpYWIheImB3dx+vvfYqtra3MdrfQ2jGaOqAJkZw06CuJ4hNA2JgOBwCRKgbUfv29ndRh4iagb2dXZhCfgDtzG3ckhJtOLUS/WcC1xgzcqaY2n/dgBtZbWCFT3sTQZGHRVJ7+NiJAY4euwdNdHmvZ4kTUSo9Zke4ICOzVOCTvwL+Qb8CnF5rtuOMmeQZmQMwdZ70hux5d+l3TqNf5KeVdnDiO0QnN+8TOcbKKY+wP8Tuq9vgxgs+Wd4Sc7q3THmSloEs8KF2hQXjwi7fkQSWvbdHZPdja8xZ37eKunPaoR0ixuUWoXQHuZwX/kRZPRUkmzHQzlhkSmajA9evX8PL517C3u429vb3EEItdVIDAVEcHsPBEM2kQT0aI/QrQbQQQESY1DWiA0ajfeX+ToW/EcrhVU9GKSnLOS/uI0vHNnNzTipPzHqOeHUsnaEN+3zgrHnRG0TSM2JNNhTzkvpDDNno71X6KUNUAoxsedmQ/btk907vQ0ljBGRjNWlRACrNGptrqUpQIYEPhttiyalVfyxLUPKDGagarJ5dQr1bY3wlwgVNR9fsfyv21cZF4xxGvMIRLdoDlLRt3EVczpKRASlVUnpMaVrCJg2jUA3fqp3VylyZSaN5tiZlnSPZCEBAdgIV6ioJYlnGcimJk2TniNg0aEJACBGTeowYAiqnFVqJ0K8qLG0cwerSMgCg3ttH9IQQK/R6PXgPNJMJtjc3ZS3VOXLHcElZU3QbDE5aTBUMFE5p+TqjUeaRGUebETCb2skISc1kydVOVFXqK06dOg4SSdBCboX6a6eTlXhbOnFsBCnHO41GH2kRiJjX2U5wb1VZYNsjLcJsUT70AQ4hay6nIylUq6UGRx8c4uruDngnSFemhkwtfWmfcdG5LjRH3TpDqrMTku1GpopRq7fcOlIL0/RzJ2GDzELkbxGXntHaks9x1G1HXfW51CfyHFv1Y437KgGcPn0ap0+fwpsXX0cMEbGJoH5Pd/MDS/0BHjxzDwbDAa5duYI3Lr6OzdEEzvdR9ftYXpK6Ore2biFGwPvSLr791mIinbkf3IzBdZW62X2IQKAWYcjtpv5z53oGVFtpmVBGzHYNJAyRCC8JBBMepbmClu+gbNR60yJXGB1Iua+iR85dzQzRFO0Ah1Ab6UnZBCGCXYP+usPa6SG2x2NgYlte831ZwS1H4Np9JsCo8V1IDxGlWbaKGdvurY3+XDxz4bwP39LE528FK+Ny5qvGvKttRzoTZpXi7455ZXUNg8EAsalBsYF5wkEORIzBoI+TR9dx/Ngx+FDj6tWLaHZGoB6jaRosryxj4+gR7O7tYjKeoNfrZ8l5ANOaFXS/G5oIgFLFST1a4jmMhVkif1y8DY/ZSmWafOCCPIwl2pMIrOdRpwnNGpsS/4GWodrhVn2BdaNEnpV2qQhp33XrM89qiw8yUndzmRdjAIggsPNYO72M3lGPhmqwHu9naXk5i0QJlDPQzDonFDEfIKG2BIIztNoV1pGIrwVbkzgHuKgP09KjUoX+w6FkMfrMeaf61d0PnTmhuN7UK/IOzguSOiJ4O2hHd/EQExwzlvoVjh/ZwIkjR5SJRoTQYDDoo1f1sLa6Cl9peZjF8auZsyq1iDtv5YrNes/ItZpMq5pmB+2QTfFK4MzfETTFmPK3Lq2PQzqFu8Pkc/8HzznfTfl9Bzm7DAKqMi8qtnaIiu9KQFQim6mmEdUa4fgja7g82kTYbuCDZKJkcrYNp3mAbP9cyZ1JiZ4LICd5CuOmtlScqQbojMxU0DnayKEaQzvShHsqvamdRmk1Oupp2VcxR0rvgDZS5G9YO/bOiY1JQqQhBMRGwggeBDeM4NAATYOBJ2ysrWJlMMCEGcN+D+949HE89q5349777sfS0hAxql11SAItyWexU2x+S6lvhQ2ZvdjFGDq8aroiXrtPu0a/mCnWqUSClvop48joxxlxisFQ65pZ8CKU9m0pHVt5x+hgDpURh9ntwDgnAYipml5GLUlPCgiI6K33cfyhVVw7t4NmN8Kz8SbAqXcscUabAVJXSCNMSeI6aDY+p6MxkyUtWXtmomHEYuHfqgpmcy2F5wwmxQqrOQ+zKSYN5CCiIFHvpahxxMmTJ7C0NMTOFqdzQnwTQOzA4wn290fY29nBa6++giuXrmC16mMMAoWA93/nd+LRJ54EkdfarHnIh4ENodjehcxkZ6gDCSppv2Lp0CkJr+UFEXgka6a7pqpxAd3c1u4M8m9T6mBSU+0DoW13E6zKu1xXhoWK5xizKNdPCETwXSsNJkOOre+sd2RtQPpZFNg6IJQC2TGi2Q4BktVvLW/8bTA8VmHl9ABbb4zhJ7KhNFXPLojFQjI24fKYwUhB552VwtIlbht6CdDCvJ0BE2QzK9qS606aLJ8gRgoyz6F48SzOB3Kp8hxEEt1fmxDx8CPvwAMPPoQbVy4BFLE+HCLsjkAErAwG8ARU3mF50MexjXWsbRzFfh2xzQ2WVzZA0MNhDUGJ8uvAlhIyVVdx2k32bFrxcJsAIyYv9NTsWiKEO3/bDC/toumMpyRMw5dcLmTe2tsz2nuL2pca82yPxYTS7BXm1h/ABGnSG1Mf3On/oGj7YuIkBjmX6nQxE9jlHeFm/EYCaNDg6IPLGO/VqK82cIEAtqoriweROUk55XYwP008/ZoYav6NctGwuYx99ghmamzOHDxyjFqSGq2MEdj7t8YM0khmZDgdO3kSP/ADn8T5c+ewt3kd9506ja3L10AcsTHs49SxYzh78gS2blxDPR5jafUIGnhc2t3HsWPHxV+oRJnjd4cZQyaEAN23mpgQFZyfUDEDEVqBPz0w9dKiQwvitifa+WhxR4N6cY+aGLfNf6kkpDmJLsX/0DmjBY9pZiFQUsZj28gSDmbcTmRqjP6tpO+R6txgwIPS3jwZYgF4a0PGiXs3cHn7JuJehONKuWw5TPPEdW8uiS0DoQvCtLZGJN3fFk53drNx5Xl1gVYwj46HsTPcu9LayhvDOY9T996D933wI/jq5/4A9548hVPDIQiE9eVlnDi6gWNra7j/7BmcOXECcD1s7o3BS6tYXV3FOBq4zb3PKQ1t/hjasdAkDSxV0+gWHbCgBZk0ofTMFnyTiJox80KKlg8CQ/wAd5LppEx0YfEki5nKe9uzOddxo3EW22UCNhjnOcyCR/nLvHZw3VpG8v5nq9N0b30QR7AjBK7RP9rH2r0DbL02Ao08op4MZjr5zDmSgZ/gIiFSLL7L88mV/6wvLQ5cNFdc34XJ3MbdD7KIkWNrR3tWnGLnVi7Kcd6FVqrQAECEwWAJ733f+/His1/Fg/ecRb85gaZu0O9XWFtdwfJwgPWVFYA8GA512MQDx88gSy+paC4SaHZhtS4UkqrInFLUcl1ZgAz2RchB9qa2NZ+iQ3BHrbQfp/GCC52T2gvalTjMhSo5n+hmv59+LhXInRJaVH2eYt3qG8hsL5fVlDuUeLScJltfuotrUVtMnDqwyFbPVesCIVcb0PHpxRFNv8bavQM0o4DdN2pUoZ8XiU1R6iBfmjBNrxsSc0IJJ/HklapN3owLEIij5m4iMZKDdPwCFZILn22uaWF4xj13UWzmAYBZbEkiB7iI5dVlHD96BEvDHo5US4ihARAx7PdQOYeedwjqjOv3+7jnsceQiyZnxXxRM4K0wH1S64q1Ng0jI16bLFrsrCskOV/VRfXy0lbyZyylPJULqveZqTU/Mnj4BPs8+vQ3qQaUibU15yIRXq/PTxBvf0nkRsjtgNt0O1ByMiBxyuTgkdQxk2JRwaKZ7ABH+KHHsfvWMb51A7wdQKGCcbzZ3IKMMaFcLCrfUV4EQJ1RhDYgFFccMygyQi50diBhlk82wLlyZz8DQDu7JvV5N2lTF5IghOC8x2Q8wjPPfA2/8dn/gAePb8BVTrULCaPEGNA0AXUTAe/AMaJaWsbRe+4V37VJu0MUxzaJmVUu7miBWYqUXtl0dcHUpl0o5YqWSG+/CStsZ+6Ys41mElVWvvNY3kqzDfUlFpb5sF3ZzEDydQDIpTQLxmHbDTNjUejQrDIDuR3qOAZZC86dtlmD+O2MIxAjIKC/XuHoQ2u4dm4LvA+42E+pWHfaKM2vQLJibWWHgHE30uT3ePgFM0gDhbd/pnX99jU2SS022vlXXsJnP/tZ/M7v/CauXX4Tf+37PoYHj63jHSePodIDlm7d2sSbFy+BOWJlfQ2RCMceeAj948eliLHFnXlxpk3ZSHNEWwzykBNop4oX95EhOkvJ95YEVClC2Qkk2sOMzPjuWA8xurllRubOApDSntT6rnvanu1CIisWzYvHQuaUO8RYDsytZeoAN42yfMNa2l4rBlBAcDWWTwywdKuP3YsNHFdJzcwOoRwPSuUEu0ymJIpCtSGwhk24UBu0WQa9Sk7L25gGR6Erl99ZH/ZN+jknPnQLccnwsiSdlVw+V45ksQOGnIjVxIhnvv41/MIv/gK+/rWnMdrbx6SZ4Hd+/w8QtzfxyN/86wg1o4IkKtR1jdXlZXDD4J7DYH0D1OshRD0+R1MQ552WVnporVKEJexTXgyA8q6WLFvLNbLenTpS8qlhlJa3bbsaCDrQ1GdMH4jVeuYCJC8l8O3K01kanmV0tW1p61yPFEwOJCQnUSoEVowsCZo7zxBSkaxATaa8LXR5TLt+z5CgdUSE6zVYv2eI/a1NNNuNxL6iFY8u/YGlP9fUgzSCDDDLt1WVyg4YYtG2xRRngKJLfUylyHVgkYqSFTaBXJePZzA6tCMorMJDezOActDEd3SRSt6GjEvJdjMhoYMjAPt7+/jt3/wNfPrXP4NzL70EjhHeO/RogCaM8Mobb2CvnmAZhPF4jHpcwxEwqSdyoE+/j2rtCKRKf0haKOBnIEoBGjvOPY2209T5UehPCouW+qKXSkmXoDDh7i5z6iA650JmabOfErTk1pblIqwbLj7MUHvTwhmgp7dkLG4dvYHa35VDyQdgEZLjy57b4iMZfvnT7Lb4OAYVwdkDWkgpfTAntbE42xAMcEDjInpHKhy9fw03XtoF9iuwYzTUIFIDYp+4KCMkyWwev0zA1nxGnFamRcHpil0MlkjNBhmFq0MKKAiRx+I5KZhtgSOT9PKQMrHCHupKRZvz86j4zlpraQsENUWEwBjt7+GVl17Ca+fPo6mlaJUdWUdU4fL1m/jmufN48oEHceGV1/HGq6/i6JEjuHHrJh5/6imQ76Pa2UO1dQuDfh/94bBlKZfYwmlQnJI8WrnU5XgVuezXjAZZrUu/pu1h7XpL9jzqAga2eyQ/dC6TaLVSgnevth448d3DWjiL1ODyezvM0nAqMwkqZY1+r74bvdEhn2Y2qy1MfM8FNyjlgzAIaV+pIZZNvBhQhIY6KGDj9DJWT/YRqxpMsvs8MpIEyhKLMFWiXq/JRnX+Pv8zMi4QShHOksAL0Mr3KMVaCSCnqpTTqxjpCHsCLKdXRhX01YlYlXpU4pZZN5DhGxMh9CqPna1NfOv55/HM089gfX0Df+/v/328+8knEUOQU5tVPYzksDVp8NXnv4m9JuL4qTM4euwY6skEp44fx9LSEp55/hv42tNfw5tvvoGvfe2rCKHR9UFmfsgqJ2sVgBbYbT2JCoYEpKryREVJyAK2SV0VGKaHKj4wQmFHGtMr+8iakThg5jixjNIYeR25zcqlpyK5/ZDJImm+NEMal3PN5KjP083bTjCyZXm3yhIaQ6aFzGKh5PSWIVMuDsoqQZT16nLQJItDunOC+wFH719DvR8xuWIcuhDtRpglgIqp2cafsmpCBhDS4ucbYrpLTngKbeWBDTSsks5UWCuRb6CXYLfFsbJ3Ngecu4pJazETzszgjixe5cgRX/v6M/hXv/RLOP/qeZBz+Pt/9+/hx37sx/D3/7v/Hs888yz2JrsJ1pEZNTOefuEcvuupD+CJ++7D6f197G3exNr6OlxvgM994Ys4Gxy+5we+B7/wCz+PcT3BR7/rY/C9fjFGkXUCpNjh8tlDmqRPK40SbRxnq7aYVJ5iTQggqaIuB9waky2cUwWh5cyrjPaHcWOlpS/HUCzELDa8qB3WgWSnV6dC1KY5oWOVs+GVVK+QUS1uB1Z6yvpx1ksKZSE7DzoT4WJhg2vQWwOO3beOauCT4ygHj2kKahlP2nki9rgMgqzOZMld9GeYlMosCARbFRiAxFVJ1ZKsdpkXsSBeHVOEpC6KdM+qcvcfkZMTtKiD0QRcvnQJ/8f/8S/x5S9/BVcuX8Hly5fwi7/0C3jx3At495Pvwoe/68Oo61qezKRlHB0uXruBP/3KV1EtreDUvffj7AMP4+T99+ONK1dw9fp1fOv55zAa7eGRhx/Gv/z5/x+eeeZrIBbE8F6ZDmwBkcRp3ibokmxI6i4X2gkr8lBmTzxFAgIlkc7ld6qZkPVv65SfY5rPYR2sJW/pSk9BAzODDkue7fuyc6nEuyxo0tMSvzEbOWucOEBSdtvi/ZzFO4bsTrEdKgEO+TBcAGQVe0yaBIhZHBFiQE01qmPA6tk+GsdgqtKSOrPP2A5jk0mX544xkfSFJvUtiJ9ZhSkTMkYruBVzCD4SEKKoioGlTL8yFrZCT2xFn5Qso6po7MBsc87zpkjpWlP3Wi8gbyjUZksdYsQXv/glvPzKy+j3B+j1B+h5j5s3buLXP/tZ7I9GeO97n0I9GSPGBsEgorzmz7/xDXzluWfhllYwOHIUN/Yn+NyffRVNVeHm9eu4du06nnzySbz+2uv45V/+ZZw795LYsJpBQgVyRaiZ4fSlEsF2WRSDBztKnvAI+cwgsHPip3CkhEeJ4KfLceTjElqw01UzAj0oUF/iqslkk16ZmRu2LKwqdWCbdt5QmocjB1eMlYsDV2La8aLzbQml+XM7IJRi3AJ55iolCLKfHLoIVNqEnJ0p9lWkAO4zVh5eRg9ArDyoBiidVA0Fatbl80A6lo2thFc1wa4mcwCxqOKuUHEihADV+UPFQiW+n5wXNo9SLk83Lrezm2rWvqJlCpRgZDAuX76MX/nVf4P9/ZHMPQj6hGYfv/N7v4fv+4Hvxz33nMKRjWXs7o7ARPC9PkKICHC4srODX/nsr+PWaB8njh7F1599Dl964Rzqqifq76TG2toaql6Fr371z/HP/tk/wyc/+Ul85CMfxqPvfCeMk2c45PVOOSw8PXuLgUaXiU6ca+1tWFPElRh4KXEI5PRsamOqumxJsk9pVe3vTIOblUFpzMy86Xfa2jtkkhKNUnebktxJM8jKuWtJWmCRfFxMnMYl2brJ9hgp4EW1cap+GpHYJEyVhMrQCCwxohtiN0b0lYukAs/WMVtuLbW3jBXgsDmVFi8BWhwUSaqSrlpLFW/z2eLuQo0tv0o0XixOBzlk6J3Fb91bwFX//+3f/i1cu3YF3knZjMgRkWWP7JVrV/DiuRfwznc8iJMnj2Nn5zU4ijh+7Bhu3ryGehTQAHj56hX8q898RvZ7bu9gv56g55bRqxzGkwlWV1fR6/dQb+3gxXPfwquvnscf//Ef4p/+0/8Nj7zjneAojFbU5mKOlBWypBmUMIH5IwwWGSalkpu3RhaZRgVTE7gJ0TqQVE8vFpswvecxI3dhPJnlQbPZaXk0yO200vYs7eBcztW1iZLLNzKwQs9Kgs3aItV1sc1JKNQLwI5cyEitUmhG5o9TryeRl/fs4FhKbOyHgCv7Y+yRhmsA9cwxShpIQCgAlYmHkUMbhW6v6pHt9zTQcBIB2gfpoalqY4mdqapJ+pzft5c792HXmbLbtjRMpZKWuDiAq1eu4I//6I8ACEHGGMSmDBAVOkY8+/WnMRgOsL6+nqRYMxrhxPETOhWPSB63dndx8fpV7DQ14B1CiKibGrs7O2CO8J5QVXKQTxPG+MY3nsW/+tf/GteuXYeZA0nSkZ0cSIro7fhiaY1bKCo3o6isVMrGCRafU0cRSbxYv28xh4Rmiwkq+0QJrXokii9pHe+AMMsWO55gYbqzJTFB9ylb5IFIPcZOf2vvU57XFhJnsrt0MIJbOcAPQGwTLfOYB18gLpk7PSN7cIzNZoKrTY0RnJxmZoNlQXW7Nnm5uC2tshqsqkOXv6YF59Z489wYh/HGzYSL/bM+SgI051iZbVCMiRkgR3jmmWdw4cIbaGKDcTNCExrEGBEjgCDqz/PPPYvhYID1jXWBBxN2bt3AkY11+H4vwZTBaDiPqYkN6qbBaLKP3f0d1M0E4Jh28DQh4Pd+5/fwHz71aTRNo/0U7hwb/sxQU2bMJgt0okix4II9RbazSGxJxH43hs+2RjG7lWYpp0j3519LomtLrXIitmJ33rope1PjU4CVp8u1pW0hW6mY8wHtAIeQ1wJShMpIhgHJ4JD6n4mDJrWhzEdUAtNXJEaIAYEjGgauTSa4xgG1d2kwniTVIKkNJlaRpZHwcuVIBagW1QAl5+SgGmcS4a21crnVOtP3NmROdVrN1iWITRyZ8eqrr2E8miA2AaGJaGKNEBowRzRgEFW4dXMTzBGVEmKEQw3Cq6+9gY0jx9HrVWKzK1dmAIEZTRMxHPRBBOzt7wiMnBV8cXDOowkNPvUffg0vvPgCMqm5zFMMbrCdP1m8cZcZ277CgiE79U47IhVoxXunCO8IzjnZ1O6ylAHJelmMexr4SggxZuFRtK6OYxlHaZPGIVsZ50z6UclrORtVlHwf3LovY0RM7w/LKg6UnNa/lkkFAfCwjUg6dXXLkwGW2qpQa8JMor4BGHHA9fEYuwBqZyorinttIpQ+AUBMZ2y0uWyLG3U8puYFhj/84sxtpkWUtnVrjvp7GYIopOvW5hZeeeVl1E1THAcghw7FGIAYEJkxrmtsb21JymOKkznsjyaIzGJPVj04L0cy2hkhDMLq6hp6vR5Gu3vY29vLwyQGxwYxTnD18iX823/zq9jd2+tweLsWsPKdCRVsfSnbWgn3onq6Y5SUO/3bfUXOc07fMyNnQ3Cxxm3YlqjdMnnyssxA/Vyu8jZos9Uy64VKv7yuCe7dh6vGyC2z6PBi4YA4Z6lO2J65CM+smTxiS6ZzKwFkVyqKe1ujBSCckgHsNg2ujmqM2MIzbVWU7E3quwQUISsOhXqTHp+fTuUPXe9vl4huW93tqtWFWgtV/dQ+9r7C5Tcv48UXX0jxP0rhnCifYwQQ0YQGV69fFXglo40QY8TWzVuIMWLjyDrW19exvLyK4WAJq6vr2Fhbw6nTpzBcGmJrZxvr62u47/770B/0IUcSBDk7JNZ45s/+HM8993U4X2W+btylo7UYo4y6FC2ZZbZ+QkSnIhIJSeEc4Cxbx4l0dM6M3KKQmC1buarcGssUjdG8Hzqq5R2aMvKMxIUSB6YiQjDNGkrzrhQ4h2uLJSfFFHCw49NhQ9DPDCeFY2LmeglRS0OP8wJH2NkUABxhux5juwlonGatkLnwLc1OAaIII7tR8mTT2kOrGqnUsniScVWxDduK0B3ZnR29qbWfUb+0Bcm2NMORw2h/H7/zu7+Da1euKjaqOhmhlQUE5pLmGLC5tY0QBM62rxYsTp/xaIQYGcPhEEc3juDUyTNYX9vAyZMncfr0aSwtLeHS5cs4eeIEfvRHfhQf/+7vRr/fl9O2mOErhxu3ruOlF15A5bQuj8LclXNVr7czQCe4zYZd5s8ZYWUGBE9OX1S4mmRdXSrapshMtlY5wYMYLYTPTp/skMv95k3N9umOm0p25pj2bppElGfrpAuHlNWgSqAoaOkwI1m8KyVq7IlQLJilhNuuBLU1CxWEdQ+hDLwlT1JIImddECYccL2ZoPJDHCWPAUdJbVMubde3lRjAxW5faeDpmrbqS0Kgh9h0fFBLdliqEIEWV+6q2A4S2vj0pz+Dz372MxiN99OwWDdCkzKtyAHi3iRsb21jZ2dPLB5VpyzdtK4DdrZ3QPuVIDYITV3De4+zZ0+DAHzpi3+Gcy+eA6iHH/qhT+L8+Vfx7LPPqe0IjCdjXL1yGaO9PVRVlVaspbImk0bgV7oD2mYI8icLPMLLPa5tmrQFjPkr7B4UHNU+tvWlbkuq7oxvi9RboCDW225dzZg72EUtGoTN1rXwzy7oxjun2wFJCFaSRL2nzJrLrO8ZYApJl2S9Wm6LatR3O80zFAQWQtpqajAIw6Ul9NQBZc8RjY4Ar2tGAKLtYrBFI+t0ahb5eUXCQxoOtd4fRq1t8WFN0BZHpSKD9mFdO1eBmPDnf/5l/IfPfAZ7+3sQAyHAsJDY8n8lYyrGgOAc6ibgytXLxdPtKlFvJ3UE6pDHAEZDIywN+hj0h3j++W+AI+Hpp5/B66+9gclkLAkZIMUVxn/5/T/A0vIqPvGJT+CxR98Jdg51aPQ5xhTV4YSc25EJwqSFajZEeRmS3a8MtPQAcwHPlMTSJr9Uc4g73uE5oYgu8RotpGOeDxHCmN+cOaTlE2l8OKoIcRC8hGzscCRCK5achmbh6Oy2kDiX2EFL0EK2dAmIQ1JJChuAvBQ8RkYf+VOGbrWROGgcbBFlQjuhwc2mwZKv4KOSLZGWJCHTeJC6T48o1AVTY9lioF1OrWNuqWe318pbuPiS03eZgIgBxw6vvvEafu3f/Z+49OabulRRpVDerUHqAo3MCLFBzw9B5HH1yvWkdkjg355iqrvy5igSKjrCxtGj2N7ewWQyQb83RD2JuHrlmuKGlDghquDAuHb1Cj71qU/hhW++gL/z3/4dvPs9T4nHlYx1aj6VIpaMVuacLJdieVsgnbdZIYkdan2VWV8RpCGFr8J4VoxwpuQsQyB25g7TPLo+ZOvc38EfUgbAzAiqAaZtXJhOqFjUFhLnOnncig1qFzMSEQvnLMdnqjaJJzaRhDEJFmSUnRVKkKWBrDfUHHFtNMLq0rK42PXIblDBMVv3KoElWyxfAh2vqdGHbXce+yw0sZIROIet7U38f/7F/xtf/rMvo55MwGZXoiDSctk4gmODo0ePIIaA3d099HwPbc6ERJxgtM5EWh4u4fHHH8PTT/85Kl8pvBkxktqNapawaDeMBtu7m/jil7+A86+9gr/7d/8ePv7x78Gx48eEqUbRCqyIuPzpwKnjWbVlmr/linJf9g0t3glCC6RORwsuhsWta0omCswVwOUd3Q6nnpNnru8YmjmlGWlFSGjeOGe1hQ6hjUEfQ85p3tnSVMHV0sGnB53OSGFGiBb3Y1W/HEq2a8H03VDj6niEPbI9pJiRgZTVH5PdRNA4GqVzXZIGPcOBkMbY+W7xHj5MXUPFC8VfUeEiRvt7+NSnP4Uvf/HziE0N55XJRXVwRbGsJfHewgcSUnn3u57Ay+fP6eFFxvTMORbkvuQsEw8iEWFpeYj7778fz3/z+VwZX6U0JbunBKOuiYu4eu0Kfv5f/jx+9Vd/FVcvX8Kg51G57F0XxldgQhIKlD6kYhW69pjxau8SMaKP8+FvDjbz9nZ/xlSthNTKjeaRLKxWnrVTAgOYJh8ucoi7v9hcjdGYEWCuLJq6/rA270LJebTXw6QZoI41xgXAEwi486BSYqTdDPaSYDmzLzQazepMZSwdAkXcDDUGkxqnew4Dc0BY4iRlgABA2htafG6325eat1PBbR6nJ1XZP/f5z+PX/v2/KxxAYhLIsSWGlJxgYoTW73s88cSj+Pef+SzM3pJrxVmUwldJxXVwjhFjxAP334u9/V1cevMiTBs1m5h0fpaFxQxwgKqqBHLAjWs38KlP/Xucf/kcfvCHfgiPPf4duO++e0G9fpLwRdJfG2EZhRjX+c2ATlfSLoBwSyuhokcq/j+4GdHIezUKpu8uRFxe28x+2yZTAn+CR3oa5zHnfFo9x8fqZR3QFhJnnxjHB300I8ZVFgKNzsEVldlyXnMBQiqySji2QGkqFoHzro7EdQE4j/0QcGm8h75fxQnv0LOwTekRJoh7nz1ibErdAvnDWzIuDmyzCVOe7ZzHc899A7/4i7+Ea1evaSiCtMBSDvEQxDYxbk4cEeMYT73nUawfWcGlNy+qzR8lpuYAZkIwlR9i/5EiU+SA7/v+78FLL30Tly5f1H3nKuecIKMnPSbDvNYxgikCkL22FIGd/W38yZ/+Cb78la/gnnvvwz/8uX+Aj3/vD8B5rQ3oCPlcpIIITQorNAguw6nLx+0/ZVrTpGJ2ygy/BTpLvqAZnAENbTEQi4T1fFVJjJ0HscDHWoycNAYGp7CZOIhUC7A5mQY3Aw6L2uI4Z2QMncOxQR9DUtWRJaTiQWk3T1JfEp/gJNYFm2xwSKqbWVkKsgQcYnE47SGKesuMqKEI43hABnbp82lD83a0e9xRbHqWumzdXLt2Bb/wSz+P1187D+8rOCJwFMkmOa4Ww7WYrlYu5Ihe3+O9738vLr55EZPJpJhjLLDa+sgpjBwClpYGeOjBB3DhjQsY7e8DTvdlRtZqlIUUMKcYIsCNbAXTcHXV68GRR10HvPbq6/g3v/IreOaZp8ExJkR0yiCzomEIQek1W5NRhY8k1Y8ytsxamal3ZjIcdnnlMsmiMmJpy0J75RIsJQWlGCtK5bfYHVqMI2U/FYIkg4fS7yKTFlPpAbm1ER4Ra5XDyd4AS4B6n3gGYCjNqS3iMzcyshJbtLC70tgJQUMTDRw2Y41rdYN9iMqbHAxiYE6puOVYylzGg1oO6dwBhcJUa3mWI8JkMsanP/1pfOkLXwBigIce6xAZ0FqzZsElhxCLogoOOHbsGN773vfi6aef1iJoYn/HKBUPYwvhDUECQpzgQx96L8gTvv7M1xGDPGN52EflK8QQENImc9IXZ1MQATE2aHiCwA1iskgizr10Dv/8n/9z/PEf/zH29vaEVZr3WAkx/Uu5tgUSFg7Ag9alrQzne3Ni+e0ptO2+b3+Nk8lUPH1KalNh85L4P6bUff1LUZnXAW0xcWpQxxFjo9fDuuuhUsISb1/OjrBFkr+m4lChihauJI3/yEezIpRlMydO2kTgxmSMm7FB4/JCUx5gsegdSCWl+3ZsksNdOy8WymAMhn28+OIL+IPf/y+qClmGSJlDKuQGhEQYUJU0csQjDz8E5ogLb1yQkbFT13zpdHAqNwVWEQzvgafe9R24/OZlvPbqq+DI2FjbwM/9g/8BP/szP4vjx05gNNrH3misWUJAZAeODhwJHGTrWh6UKD7eExoOePXVl/C//+//L/z8z/9LvPjiOTgQer0KVeVhPgZzjDBnZVISKzJP5wMQM9ngJQ235MHtkViygI0R3cb9XL4xb3XSZExfRaF668UdvCSI8CFnGU0Hj2BxhhDJRIgZy9TDqf4Accy4GWJKXWKLQWquYdRFicxao7YcHmDFsVg3VNu8rUhS0D1vpOrRHjMujsYYLi9jgzx6rBk+Kf/Tlat2W86ct3IPgJkEevXqVfzSL/8yXn/9DRCZZxAZKVKKng4/VSJsAA4Y9B0++pEP4lvPfxM3b9xKaiuDpBMdqlp+igQi9R948CE8/M7H8J9+8zcxGk/g/QA//mN/A3/7Z/8eyHt87GMfw6/92q/h85//PEajUSIC2d9syprs3JGQizowFLtjjLhx4zr+469/Fs9+/Rl89KMfxfd+//fjgQcehEtrmTdxWegMhJTbzDD7+GACzXZrW86W6uVhVs4ksRFOPmn9kK3EL+sj64HJ3SLfFtln2UvZ7qC9lHPbwYfnKlArYhzxHr6/hMlug30Cgis34wB5ygRWbp/19xw0Tw6jFBAWFTcWW3IIDDigBhBCxKXRCP2lZfgCQDL/AKsH+hffCvQgYDwe41f/zb/FF7/4RXCM6HkvdX+iSkVoic2ougKr7aSMyXvGBz7wFO677xQ+9+/+BPu7I4CWkhPNWsrFMh2KpC7Sgw89hHvvfwhf/spXAQDvfd/78I//x3+MldVV+KrCJz7xA/jIRz6MP/qjP8G//pV/jZdeegm3Nm/p7hEALBIw5biy2visGasR4Bixu7uD57/xHF46dw7/+T//Pn7sx34Un/yhH8LR48cAEJqUucUFERQ2nEnmuUzRtCouP4FKdKaFSt+s3tJqGS86LE/mzt+yJlp+Q1nbSBhaynogYa7jMr4zty0kTqfqkiGSB7DiHI5XA1xpxtjXp+Zq3gBM8pVqYlqh0gEPkNVAZXRipvJOcFISH7abGtcmYwwGA1SRrBrJHdkQb7XlYHnBkyPj+eeexx/+4R+ByMFXmi8aGRyD1uplkP5ljVFKcL8BKODI2jI++uEP4NqNG3j22WfhnDndSIt7ZgeaJPkX2SrMeOqpp/Daq69je2sH3/HEu/C//M//C1aWljGZjMF1De88XK+PH/jBT+Dd73kSf/bVr+J3f+d38MrLr+DNixfQhIDReAw0pKoqgODEzyBTFEeQA5wnxBhw6c0L+KVf/kVcuXIJ/+Af/BxW1tbgQs6Rikn9a+tQi1etSBqcEfdcfPzP9Fr54nMZVDjEzUjHTha4m7FdCFL4jDELi8Nq2EsFjsikKT1yodZ24EFGxbTALF7ao4MBtjlgRE0S60yl4oBpUW4TK+0IV9S/7WjhrBO0Y+7HEbiyv49hVeGE83DcLZn1F9e6IPauwvXr1/Dpz34aly9fRp6wECZzjWA2Z+QUBokpvYtRVQ7ve+97cN+99+B3f/8PsL21DXJDpPMe7YlsdZuKGgDMWF1bwfve9z78h09/BmfP3IP/6X/6n/HUk+9RO0v2FNYcwVL4AEePncAnPvnD+PCHP4pbt27g0sWLuHzlCv7wj/4Qf/6VL2Nnd0c0Gw91jAYwPECUDlIGGnCMGE/G+I3f+I9YXlrCz/zsz2JpdVX4Ehgz3RpsPJlb38m0cviBWoSgl5UIXiD2zLBWJ6PoTs2XLDqFUE1Vn76o+yW135kqSpridwDeHuKUMSU4FgnqCFjyhJODAepxxC3ShG1OhoXgCzLZEM8eOkC56rtQuBrbFh6mxIUCAeMYcWl3D8PlFWyQqnXxL5YwpeWAjmWt/Omf/ik+//nPI2jZDyYCgji55BBerdquZoBtsmZExGaCex64Dx//+Hdjc2cbX/jCFyBb2r0+Dcq+ZIu7eEF1EzRLdYmPvv/92Ly5CW4i/rf/9Z/guz/2cbhqiDqaVhLTuoAJgQiV89g4egRHjh7Bww8/gqZp8Mkf/mE88/TX8Fu/+dv40hc/hytXr8IrkTmtdytCXNhqVA1gZ28Hn/rUp+B8hZ/92b+N5dUV1I1UqzdEaNOGmj9TrE41rllV9xbEuzK+FUrwnOuFxnISwmH6JQ0lGkM0+izxOv2WtMbS3KK8+SbZ57OpwtrBxNlS4cwGidjwDo3vY9SMMXasTgVTV5VjmnfLdhaQlNqwgdvBQ4imqolkTeo4ASHDGuwdtmPAlXqMpX4PS5A1jAuA+7Y0RnJYMEe88MK38Ou//lmMxyNRhcBAiAihQRODbnWNqZKvOLScnPxCEb2Bx3d/9EM4fmwNv/V7/wVbW2PA9ZAC+KmCvcDTkW1wF8mw1B/iwx/6MJiBn/pbP4Mn3vUkIlWIsSx0p/a+MpQYIxrdTQHmFKMdDIb44Ic+gkcfeSee/p7vwe/97m/jq1/9Cm5e30TkAB8ZUZMYIiCbp0FwjrG7u4tPf/ozWFlewU/8jb+OwXAoz4IF7DMB5iUTXKFOkafbdNmUPaHMTZsldZOfhGfbrV1iFc3cJ5MtcoQjl6MSzqetjWXEiEBpx0pWjSk54kw7mNduQ60V6ArnjeiBcKI/xCYHXI+yvSgUHNLB6XYyBpMvBqj9MOA4aIzMw3bQ55xHM0IpGaSRGNFFXK/H2PAOla/QM+ruAPWOVZgDWubMIvk2tzbx6U9/Cs9/8xuSx8riXHEsMcMYG00+EBU3aBJGpbZUBOP+++/DU+9+Arc2t/D0155DEwggq9qUQC+aC4StkSY1ODA+8IEP4qEHHsLj3/EunDhxFuQq1IWtV3LvDB+FcqZPgTEEYY6fPIVP/vAP47s+8hF8+Utfwr/4//4LvPrqq5qNFUHwIPJAiBIeiOKhv3HtGn7pl34Ze+MR/tZP/T8xWFpK1d7NfuYkwqH4ortpuLDFOnLw4HUR7Cl9HYyMBxk3ONmJXeKfhTPpCnLJJJPQlcKTO1fO0q7TTLKs5TI/eU47lMurqy4YF+854Hh/gGUikJXbKIL/rgCxVD+w+/Mw875C3UXSnWyhVkOl1QSMy5MJ9siOmOtsEH4bm+kCAMNVHs88/XX86ec+p7s75LgEUWXVUaBSNteaUeSAFEmrHPBd3/VBrK6t4ZvfOodrV69DLHuniKuxTCZ4WLnFDJ/hcIgnnngCp06fxtnTZwHKqX1qGKR/9k1gFIiWYVwm0IUYMZk0GCwt47u/9/vwv/6Tf4r3vO/96PX6qGthOhEh7a+Rog4E8oTdvW3823/zq/jd3/5tEAPeec0lpQKGElJKhwwBoDmIfZhWIrrRYXlvWeK05aycub6dEbCwyTKZ3XFZeYFaFd8ZJk8slETF3aUza/HsDu2PtpQtSlxP1K2NXg8n+0sYMODZVFUjYLOTZFCx4DRMQCSrGdQBSFKBqACHviPZWbDd1Lg+HmOiC3t4x/pbazYGEOHW5i389u/+Fq5dv95W/5k1fML2cWqS4mBp8Ng778d7nnwUo9E+nn76aUzGtezCiSJJAlsGERdJN8rWKOKhhx7AY4+9E2dOn0HgfISFnZ5q0qMrFZLiZ3HINL8u4orK+9RTT+Gf/pN/ip/+mb+D9Y2NNJUQGzRNjVDXaOoJQtOgaSbY2ryJX/lX/wrPPfOMYIDWlC2Logmv5qwdmS02x0yZt7PI1gUdPLoT7aksxIYSRrPGgM77DtwWjvsQptht4TQVKiYggeohAcd7A2z4ChUXZSsNSUt1OEHfxkdZvSjcuLMQJLlg9PoJGNfGI2w2NQL5lNRtj+HinrvZ1GpDiBGf+9M/xRe++Pmk6sEkJCICB1i9GeaoG9FtPA4cGUvDAb734x/F0aMbePm1V/CNb7wgmTrwaFWMSzBBQmRmxqA3wA/+4A/hkXc8irWNY4gsNWEZ6pCaLyCQmCeV64NCshuPlXXu9yq8853vwM/9w5/DP/qH/winj57AkvNYdh59BhzXQBwDPAFCADHjlVdewb//d7+mhzghaQ9ZWutJAeosM/jJtYctJZPHWyqPadpc4pbiENFMsCTtrCScdH/WgA43Jio+FRqTMmmTpYt6O5zNmfQFAijC6p8SC3CXAJwZLCOMdrEVG9SOkyqbFA5C3iZDQNrPSXnBTBWQ/2dwRiV6FwnsPPaZcaWeYGm4hGXn4EkkFhU7WO6+5Skzu/D66/jsZz6Dpm7gew4cIOosAogjQoyITQSjAWLTUpekno7DmXvO4Mw99yMC+MKXvopxA1S+h6jHW5QwkwWW+UQiOFfhwx/5GN71nvfh1Ol74as+QuA2F+/qdx2ubR9tA3xL6uj/DFWTmUHjEZbDBD/58Y/hyN42rly6iP29fbzy6mt4+cLruLmzi4YJwXu4ygPc4LXXzqNXOTTjpr2+Wk6Vo82VlM5iGkHbsDmo5f3B7EplnVH2YBrOVHkau7IIvyRWSnr2zyETXYQXqB+ECgMucrEuki89S5W3diBxWq5knotGJol0EzTDccCKdzg5XMZ4fxsNGA041bO1/9pgslYARp+4yB5wsP1wQAPCZmhweTzCPSurGHCAc7mmkVNIHcTrDqP+lA6FrZ1N/OZv/xa++cK3BJkiEKMTZsSMXq+H48eOYzIeY7S/i93tTZXjar8Q0Kt6eNe7nsTyyjJePv8qzp07D4ZHLHKgCqMwFXFmEI5ubOCxx5/AD37yE3j44Uewtn4UTZDK6t2N4AvnLl45y9+DWR3ddUjv9raxef5F7F54DR88uYb+ve/BpatX8fHHH8ZLb17G5//8GTz/ymvYjkC/18Oy9xjv72PzxiaGK8tai9Z67Iww8ZEDRz2zpbvyVBZfn8yQdh/lbyneqgTbxtNFjWfMcFpYHDTLQ0nOpM+TiHyBLxXqo3S0Mehhr+ljVI9TaZNse86b0iGkWysFMKZpOkh63/W6xrCe4FTVBwXJpwGQOBSQwSW9lJz1dpowpXMvvow//pM/wWQ8hvdePLEI4AiMRhM8/MRD+B9+7h/iyJF1/Okf/yF+5V//ImJdtzjy2toa7jt7LwgOn/uTL2FzcxfO97JwM9WLpZIhAfDeodfr4UMf/iCeePxxnHvxBfy1H/kxyCniaYTFjMW/W87TmGUuC16YDIkTKCMhyXCpHKFqalx67uvYeunrGMYG3ntcvbGJe++9B+j1cHRtGfedOob/89d/Cy+8eR0//IkfwEvnXsC5azfw6qvn8fiTT0oWTYphy2bxIsU6w/gO9Z35d80m+Jb0nPNbm8wOhzOEboIBocV2CC1snNcOlYSQNHouLEjKi2rg7AE4MVzGdgyIIaJ2StBJlZkCQevPolHk/03ds4kSJoi4OhpjaanCuiYdEmv5jyJ5K4NHVAHm2yVQxng8wdNPP4NLb14GwStyceK0K6vr+JEf/W/wkY98FP2+x/WrV3D6zFls72xjf3+Euq7FXuwP8fprF7C7vYUXvnUe4ApUlIjMNpNC2HssLS1hZWUFiLJj5cjGEVS+h8ZOcUcZ5TZ1p83r22Qo8c7MxMzbqAnvLO/7YFz+xtO4+txXsBp2UfUHuH5jEyePHMOAAkajMYbMuGdtCe97+F70PeG/+5t/Hb/3R3+ArT/8HG5cuwLgXRJSYdaqFzZPsxMLbQHTDp+D1kWYzuHk2mH7TtIz3YdD9Z9i4MV9YnIyLFaasukWdLc48X3K+M1ZFaVqYnN1kbHiPE71lxH297DNIVdxJ7v7cBPsPrf8O3UoERz2YoOrkzH6/SGG5CGprZTkdi6JX9piKo2TDUbteXcYGzNw6dIl/O5//j2MRvs6Fk5QGS4N8JM/+Tfxk3/jJ7GysgKOExw9chQPPvgw6nqC0d4eNre2sL29jTOnT+P119/A57/wGm7c3MopYRGgnDgscHOEXq+HpeUVVL6HEydO4sSJE/ixH/sJ9KsKITaFJpMhRejiazYsTTK3YqBpL1T+zoOxffF1XPraFzHc38Kwz4ijCWK9i40j94NCDRcm6McATw2eOHscj9z/APjWVbzj1An8+Ce+H9xYmmeR9NCV2HemzbbW8e40i4F2GZq+53JzORXXd8c04+bixyQm3orNWTYu9mESaz1OJ+oJMYQzUsRRX2HS76Me7WPkAyKZGoMpNetQz4VKJ5IUNAeHmIhdkCog4kY9hiPgzGAJSwxQjClB3hKQpTcJMmjOwIJllRFHlqyc7e1N/Kff+A288frr4nQCy/kgHEC+h+/8wAfx//jJv4GlwQAhNCBymISI++5/EH0P7Ozu4Ob1m9jf28Xa+jouXb4E7z1W19bA5LA/HqOpm7zJwclRcj3fw5EjR7C8tAzvPP723/7buOfsPXBVJaeEASDysNghQw6fmj0vK0lqjj172XzVWifdMDzex4VvfA28cxU9qsENcPnKZXzHk+8GEFBDSpwwA/vbN7HiGe/5jsdw6fXXUe+N8PgDj+Ds+75T4Z9C99ooL8kdt2ntYPr3O2jiHUre1XwO6axnyw1RS8LYpORo27aVmbO0oEbHHaq1pVdLsjpMpbTDbUIS1yYRCYQeGCcGQ+zXNWKswQQ0SIJBd7EcAJguDHQMZF49lNaJyMcaETfGe1gij6pfWQmrlmoi0kHsrYD20k1pCkk7FI/lCy++gD/+0z9BCI04u1gP3IkRjz76EH76Z34Gp8+eTR7WGCOaJqDf76MioPIVql6FwVCId31lBYP77se4bhAA7OzsYTQao56I6tvv9zFcHqBf9eF9Be8qEAGf+9zn8KM/8qNYWV3LRx+YtwKmpWbNppVvamd8FN6NlFLWWQNHwHjzOjZffQFHwhjORdy8tYUjR4/COYcQJAMsxoAQIiZ7u+Cmxs3Ll/HG6xewOQ544MQD8N7DaWVALnKhi5DnW2wLOrjNh3D5jg3XSsKc1Y95u20rZHH9DFQ/iJ1YO2A/ZzZbs0qZuQWrHpWnLwTsiLDkKpxcWcZ4ZxtNZARfSMAON5F7u17b4ncuZkkFsDpetEiECQg3xmMsVRV6zoNCzm3Mc1Kpl3Irs2qaryl0RCLsbO/gc1/8Ii5cvKhbviQdj5sGK0sD/MxP/xTe89R74HyF8WiMXq+HECMuX76MGBl1DGjUOHR6XshwMECv8lhiyU3eWFtH3URMxhM4J84fcsBkMgE3Ec57RA74j5/9DEJd46/+tR/BsZNngGCpBwbJErL5e0M4YeyzoV3Cw5HD+Ppl0K1roKpBHYBJHXBqbR1NU6dNC0FPC9vb38XGxlFcvHAeN67cwMQPUE9qNKFRCRQT3uTE83J8JcP9r92oUG8ZxtDRGW9L/SUg5SC28G3+nBaJqYNPGSuOPLCHiY1QBv1L9YjUMx+x4j2O9oYYRoKPBVEktGEgSV1VNw3x0wSL4ZuN2LIdCwaiifW7HHCrnmBMQEhqBornxuJzOaZOrqM+OoaAV155GZ///Bek4BarhhYlUfyv/NW/io9//HvgdY/jvibAA1FOHIyEGBhNI2VA7GhA5zIciYHKeQyqPipyWBoMMOj30fM92SZGdnYpMNrfw2/91n/CH/7B7+u5nw4tpCEqc0W6KwpCLs6V+c905o0D48JLL8LXE3BgjEYT+F4P5BwmdUA9CQg1AwEYjUbY2d2F71dYWVvGyTMnMFwegogRmpCgnNbUmGyCM2eN6i8BdWZFoow1GFKW5W/0lxnZTwlPyb6dLXXntQOIUzoX/JZEZzubMWewFIpAkqISXO0DONof4EjVQxUCfITagHqVXW/SzyQkQyvBqRpteWslSKggJE3pM4HaELA5nmB7EhCo0jRBA2KaVaGK6/ihyfv6yWrO7Ozs4ktf/hLOn39Fk9kDQiMFsN7x2Lvw4z/509g4crzIV1V2Exi3bm4ihBocArhuEIMU+OIgFdihEsqTQ0VSwFk2GEuuZo88hr2+bJQmOYlrZWUZPe/xlS99BZevXO4wOGNzOtFy54XCN8vZacSwXxwAFyNef/lFIEgYaNI0qIZ9NDGiruX07BBEI7h67RpOnz6DGCVIMxj0QLp7I8ZcS4mAIsdVx5CQd5of31nj4nX7t8bWx8P34ZKAiWo2qKRl0+w4OUYPE38/oPqeII/U6LRNXV4qwRkHaUlQpLqdMURwiFgC49hAPKhp+wyySSR+Oz2QJS2PEmZyt+tRMMy6haxAIKCVecJqk24j4vpojD1ERJKQAMdaPIZEiUsTBOGZHCLlsydNPo/HY5x76SX8+n/8DVHlwAixxniyh+FyH3/lr/xVPPTgQ2iCSAdExsryEhhij7507kWMd3ewv7eDpq5BIZsEgQGGh/MVer4HX3k5hsIBIEmMBwL6vR56VU+4s/cYDpewtraGcT3Gl7/8RfS8V0+o8mqS3aBEBHI8w7tt6uw0LxewMzwcYhMw2t4BiNGEiP3JGMtLy6ibgCYGhFijCTX2draxs7OF/tIAdV1LZQcwvCcMXIVBr6/njprGIdkyLgoTtu8p72+7C+3OKFxOcTPGbIaYlrw84N7WsZMsJqD8VW3T+aLivBDyHYdS9JGdv91fs0SypOF2bRVg1QGnh0PUkxH2TDLCuEip6VACSgJM4ZSi7pNbHrQsxc0nuBsDro3G8Eta1rNlP0izvEfHhOAgUkj5QuCIq9eu4o/+6I9w7dpVVZsZRB4PPPgQfuxHfwx/5Yd/CMPhQJmH8E7b60dg1HUNFxsAEZEDnJcj8diUAZIdDZWzI9RqeO/gTcI7qbfapwHqCeAdCxFXFSIFXL70Zk5cSOsg4LCEkZaK24LX9KqmSwjY2d6SQskhYtI0WTNpGsQYRS3ngM2tTRw5soHRaITQBNSRwQGyOTtGOfvTWELhJ2iPxJ57t4jzLfRTOMfmaRitJ5FtpSyvmx84zJ5fWshDbiuUoiTVetjsEpEGYwKI4SGV40cccbGuEakrQRdMnil5eD2XJ0zxdBKBPVjnPCbG9ckY/UrSyTyZMlsgbdJ+uACucLbxZILz58/jy1/+MsaTCfqVQ4iMY6fP4B/8o3+MH/ze78HGxlEwQ2xJ51KyOHFEr6pw37334tKF1+AcJdUUAJoQERtOBEiO4LwcHV9VPVG7ycGZOksO4EqI2Tswy/2TyRghit9ZrFxTm6aWrnjTzXvBNLEwsDfaT+ZGXdeo+j0JEYHA7MGBUNcNdvZ2cfyoEKdkMxIaUQsQYgR5n5mwLRM6vGLG5zttJUrOovUWzk4BQHC2JLQSK+aaAgmPbRZU/D4tWOQLN5N+rB2COE1y2SOmH5UtHZnUrG0xQwAn+0PshIhbPCkqIiiPoSgBcco7VczxZN2nGttseSzTUVOpOKDzdw6jGHBjtIf1/hoqcvBsPE03fOt9diCw/coxYn93D3/2Z3+GNy5cRL/fB2IN54Af/Wt/FR/72HdjsLSCEELa7WQnFpNqAZXzOHL0KC698brwDHIAeR17LdXzycM7J2ESEIhEuhJE8qRCW7r4BAaRQ81SAqTX68EqK8CkMSDJ35xJsTxxbfEWXzUlCGhCg9BI0v54PMbqxiom9USPcwhwIOxs7SLUDUKUcikMIAZgEiJiqAAGloZDjCFLGZFhXiaZ6xd3Vak1U2jRNeW7ZLkXMeIUc57DNqx/+z9L2i6NlFcwwLKX9S2qtbNbGXYw4S1e1pySZQghC8ZY8h6n+33Uew32i1Or5B7ZVWCeTGNEkmQgJ1tQgXCAHoPeYb1FGSNE/bAdGW/u7cMPlrGiCM4ckkOK9RlpVhwxHo/wyvlX8MUvfAH1eB/OAQGMdzzyDvzEj/wIjqwso0rqdqm+5PBAE4OcNN2r4DXO58ghcNBKeDndTuxDOWOlqiTHtvIVvLccVAJXXtISHaGKAJHH6toGzHvYDpSzqOGcQKMpj/ODFbYepHYRqKfZtyIhRUPIp443EdjavoXl5SWNdYqzK0RGPWlARGjiBL1+H2NuCuGUR/C2VazoZH3NaqWKXUrHXPRZTSBlavPC83kPKLe+M3U3jcDyiFN1/MUpObcR58zkSFrMK3v2ZAKBMrLbUQuyo193vTNwxHnsVX1caWrUPid127lZLcCZ3UXl86G1xObo67lckah+IEwA3KwbLFGNajhAPyGsVr9SBk7J3UrY2dnFFz//ebzx+qvC5ZjQqyr89N/6KTzy8MOo65h3vShGuxRSkhYCY3NzC5UG4R0pnCYBlXMg70EU21u9iOCrnuKDA5GTOkmkByE5YX69yqHvHY6uH4F5zUntXNvUXhKDLp2QcYExXXXT7onM6K+uooFDXQfUQc4VhR4Jweyxsy1JE0ePHUEdajnAJ0bUTUBoAqqlZeyOdxAdaZkWBqgsVPn2tTSnjuRsEStz61oU65/MMrYc7EUKd5HyR/Ycw1hbCM7RCdGLpnJ3u22x5GzlmHLbsWAisSQolN/LN8I9ZDCOgR4FHBv0sR8jxhzRkCg6RvjiSdXwTccwMbdCjKIiWjZGOQKCpOy5CATWagIgjAFcqyfo9/o46iRe6DnL/Ky/yEN3d3bwjeefBXPQ48Md+lUfDz30kBxZoFUchLm2Y4SWLQICQgywzb3M4sUOoYHzDt4BzmpPKgRcUeDOO6CqKmF00cwLYWhV5TAYLGHj6EYBorwxIauuOUNItvnl65KkLNdPf2UC1lZXEGLAaDKWxQgRgSdoKIKDw4U3XsfZe8+gmUxQcwMOovWMJjUaJlRwOHLyNIKkkRU6xdvfDpSYQGfjNxI+teWdaoGRW/d2etRTwMWsszm6FJe3+WcpbOGvRfA4OAkBJoECImR3v/EbymIt2UTTT2Oxh1SQE0csO+DUcIghONWfbdcOsicwyqyMqQyz4jPrF3LqlUoife9I9n7uMuPqSE8ug5RlZiVIGRunOOilixdx6fU35BRvjqDYoB7vC2GaZOK2tEHxnll6r6oKVlYxhohmUiOEqOo61E6zLCYps+kdofIOvZ5HVRF8RfAeIM8gB1Dl0Bv0UQ0HCE1IxafznhaklEpCe4DcGa39a89EJEHfOywvr8mmcZbxT+oJxpMam1tbcJU4suqmRjMRadk0AePJGOOmwZXtXdz7+ONS5S8uUqjvfptdtYAX/GatsI30s2k03b7LPiwmrj0g+6dbj84fsxyY2w44yMiphMg1UovRiPRCXuIylc5EvNCsqGcWpvXMWO05nOoPsBQBz9BE9oK49RSsyFZyg3LohWxPg5aX4rztiUmKfjUWs2T9jwMmFLEVG9ysG9SuQvQVAJLgsAGWIzxH3Lh8Cbs3b8KFCVwMIG5QeS8nfUVjGsY4clhfypTY/AnLS8sGLhAR+oN+IlhAvLymFnvnUFUVev0+ev1eqhNLjuGrLJ29c3C9Co4crly9KpLYGciL4xcTc4OuUj7svsQX8YbnO6ysj6OIRx59HOOGQewQatmzWtcNbty8geWlJUlNDAEcI0IMaJoaTWRs1sAOBhiePIM6RkQzURbj49vYytmKak5sCQO5KJ2V6EzFuEsiJNJDmUrCLNhbgf8ZO0yXKVcg2xyL0swPzBAyrm8DycQYEDmkobEmgENfiZMwWlLRKdL2mHGqP8AJ38MwRLWVgAgHp8FbZqe2pWsFdg2QzKIRxoJlRXKIziHCSWYQAewY7IS4a4q4OhnhymSMkSdEp8xHCT9OGjTbu3jg1Gk89fhj6AGoIsMFxtJgCd55pO2TSffu4oEsnnce9957jzh3qgrLK8tYO7KB9Y019PtSl9aYmNjYDOeAylsxNRkURac2r0PVq/S8zwrOVbh18wbefPNCwRjV/izHlUyhaUxgnYdJ2KToM9CQxz3vfg82l9axEz12Jw0mIWJnZw91XWOw1EeMAU1sEOIETZxg3IwwoQprDz6Gj/2Nn0LoDxLB/0WptDKltrRLjR3s+MNMNMq/Gel7U40MtcpthN152FNsO0jq2dR5zhckerFsoQW1kg5R4EuUEQcHB6mlyqyEyZonmkoktlWGtJHWBsgQFZcAcMQSA6cHS1gjBx/1pOYySySKrs+BrXh6rgELrwSYLXCjlbxbDmoPejhNPQwg7HHEm5N9XGlqTJyTY/DgwZHR7O1j79pVnFnbwE/+6E/gsfsfwgAOvmEsD5ZQVX0hZteD1JatAC5LixQSwnucvecsXFWhGg4wXFlG1e+hN1xC1e/DVRYqIcCxHJhGqg5Zd1Fm44jQqzyqQQ++L8TZcx77O7t447VXVd2W1WpVOVT4J+zrNCPalhosj0VDDoPTZ7By30PYbAh7DWE8abC5tYW19RWQB5pYI8QadTPGeDLBXl2j9j088N4P4snv+X5E31MG9hdVH3FxSww9SlkXOSWcEmEyO90UbmZLW0oC04Sf9ZCsPaaMU+2DKO8pTpKVRfeb1w4JsdJVbNLUkudIJYCowY58schKJKQhFjWG7Qg2RMay8zjeG6LPDM/lFrVClSVTCUytSJYACKoyk1qQhZRGGoONRqUROYwYuLY7wq7aQg6EOJ5gsrmJ0bUb4P19PHjyFH7qJ/46vu+jH8OZE8fxwL33YnVlBaDWQW/I4f+CRNUR1h8MsbKyhuXlFfiqD6IeQB6u6qPX66OqerqlSlIhI0jTDZVAVcd05OF7PVRVD1VVaVKDzP3K1Wuo6xrmnk/wyzNWmOfKqxlGhd3Z8QzFJmB5dRXveu8HEMhhdzTC1uYWyDn0+gOM6wkmzQRNmGAcaoxCg332GBw9ifd818cwCuKjoLRe/zVbOW/KwsK2hRlFGfzIIGO3tuHJrX6LpygYbTO7Hdg2dXp3iKB04NPsdnCBrxS8Rh5oMag8UEqUXpQgLXuC6QQpZ191qSP9Pk5AKrmPCKjVJjcVOFigNx21PGuLqpJv4tLcHbC8LwhrFBvcqmssVT04BsajMbavXEW4cRNV5dHzhIfPnsXGJ34QTz31bgxPnMSR9fX0vATszmTLdMPl4QCDQR/kRYJTjMIt2cuRCRDtQ8IqEkeUCI+RjGYKeQ/ykjFEkALT5ry6fv06JqMRhoOhqkoaUUrSsxCjKFRxRoITpxHblGxuHo++6z145Z3vwvmvfR7rVY3VlTUEQLyzMSCGgCYENL6HOFjBUx//BFaOnsStWrtPmxR4GlhvQ5tZuX1BMoLpeFQwKLL/OK+0aWUpTZVoSktEV6Jye86c4K2rO+dICOBA4tQzOiTQk+N6lNWgVPovhUwMGHlMhiQRAU6PxRFRr15BEE4MB9hvGoyZ1d6C6RUZWAUKZctTH8+MoEfTOxe1f1fcoXqxYTQTGgKuNmMMncfpqkJTB+xt7YL29sCVg688UDkM+j2cOXUaG/fci+FgUPaYujOWk0cpSNnzDpV3IG+JAg7MHogORBV6HJW9Opi7gFQ1dfoA55yk+JFLnNi5jEXjyRgXLlzEsWPHMQkW7M/eUdMMWJVeQzjoE8uEEptbWb8WGxt47w/9Nbz6yjmM9y6CQkCPHKrICHVA3dRwVGFEfTz4He/FY+//CHYnEiKzpJS25Lr7zQp629PaKK8QSTyK2gIkG5bZpuT00Xh6ixmXjhwCFXWRyj5t3pkYrGKlnTGzSHQe4K0VdYtUVdVxgUD6vRYwhh6sk4oD2+LKuCzLBCC1TdU1H1Uyc8Q6gDPDZSxBjv2TivByGhcKlSxXkC9hr67taPaEOaQCiAMITcEtMpIwAbsx4OJkhF1IFkwz2Qc4oK5rjOoaw/UjePjRx7GxcQz9qi+7RiDuq4gg/zjvV8x+P4F6QAUideIYgTmCr+QMT1KbUxxqGgc1m8f23eiCRmbEoCd3qebhdTTPfuPrcF7gYw4HELKnu4VKvlB7fXqVeGIaXyDCxHkceexx/MDf+x+x8djHcQNncKk+gsthCdfjAHt+DTh6Dx776Cfx4R//acTVY6gNwZi7q/U2tYxvZaijxAvTHCRXWXEgST4A7LSot4bYiq2Kdm321kvL+2BdkiWU7Hd1lJahl1Jxobw7alY7QHIq7QorhUkHkxS6kQv2S8l5UfxSKkuxjFsWnMVHxjHvsO16qMMYIzhEE5waTmGm2dxEoZJLkkT9XKpv6eLW50jAVqhxaTzCCgDEgKryaJhQE6G3to6146ewujeCX15C1e+JWhxjSi6Q57uW+sZpWMolHQFREINdBJOU1IwBaELAeDQCmBGC5KhWVQ+V8/CukjBT8GA08M6h551u4xPpTo6xvb2Jve09+MFAbFbKR8zadI1rC8cWLcLKvkSLERvzooy4DAfqDfGOD3wED559BF/6/Bdw7oXnsX3jFXgOOHn2LD70Xd+NB97zQUxW1zGKZDpXwoe7maY3K+tH1H8Rd6l8a74i6RB6QxaP+pkSjkM9y5QULcPRxXm6nHOs7alqYxgUy0dbKuUiZfs2TxnrfhOTyjR9XSnio6oLnLZJpWsNT1j2IJ5aWcb2di3ueT12zQBZAnD2+KRfh+kUMSpszexAVxWRgJv1GP3KgaoeXOXRox7GiPBLS+B+D9vjGkvLyyDnQcyyt9WQ14jQcgeR+RlBVFpLR2RLsGS1JYlQTya4eOEixpMaDz70IMK4xs0bV7G6uoZe1cOw14P3AfAE1+tLepMyJOegKpLDuXPn8MSTTxZONWV+Kk2yOZCJMEcIeGoVWw4MBhpusHTmOL73x38EH518H/ZvXoGrKqyubKDfW8LYe0xUFSc2B1lHB7xrjRPxJPVTZzezBCuXIqv4RZ2O9pv5hLLKm0OJC7OOTCSme7l1D7OZiNC/RvnzYXOAWmsuYxPSpqgzgADLmWUNq5QPMlvMHp/2Tuvt5QEw5pElMJaIcWq4jGUlsAQ3ETyaCtZRRxKAyrGXKkfnGuuUAJDEQ/eJcXMywU6IiIMh/PIyhseOore2iug9dsZjNI6mn2WLwaynVgMxJUUAqDzgJORisUzvPCrfQ8/10K8GWFlexfr6BmIMeOrJp/AT/82Pw1c97O+NUE8msuujkUC/xc0ErkEJDYgccPHSBYRmUjod9dSVPGYqFicFzZPqWa5FCT/5NXDEbqyx5yLC0hArp+/FyonTiMsr2PUDjJ1uDeMyQWMWW787rbWucwjHtJvuYUMtaW5qTmeks9INZ5VzSW4MtTOztG33lTzphS9lkdJ/uFBKMm6zNWU2Sfn4tlC3h5ev/Hs328iu8DHgWK+HE4MBejAEshCJqgFcYJ89vSS41lPRCvHlWw1xhONFZmxOJrg5mQAry6DhAP3hEqKvwM7DDwbo9YfI8qczd93V72IERVYPs45bEyVkDHJCdK/Xw6DXR7/fR38wwMbGBpaXlnH69Bm8/wPfiUcffVTr0cruFqk5JJUSSlATZ6Tb2t7G9s4Oyi1+rMDRYFeCGpMDuzL0UixYuXol8FRdjiQbxvarPvZ8HyPymIBTDm063yxzA8xqU7ZhaZstaInkqYsFJghinqfCPfWdcLjNPLhUczugKJXPheNVUMWEZEXhNWoTooS2eYaOl9uBxJnsDh2EVBpxgJ7poVK6kLDyRcEccnZPCsK2A68Ec7CIFO7HBmd7PZzyPQyixD8dR7ioO0GcOJGII+x8RCqePbtlF3i5BGwQhUPT89iliNDrYRsRWB6CBj003uGBR9+B9aNHOnyhlKQ5P9eQQYgngMMEoZ6gaSaQEmRyIoonyQRyzmHQG+CJx57Ao489gbX1o3j/Bz+I/mCoZV+EefR8BXJSxjA0QU7ODjViI8R768Z1bN66hUoT+6W2sMwvw4chpy4FEAIcIogCLBWyrcxya54yQw+nDhNTEAR8dq1Lfw9ek+l2EGGWI5sbvmfdBcNAzvgBiohuau0qG11pV/wtHUOIiBwRY0TgiMCyTY50A4TdKH4SUWkt7c8qN8Kcp3HuLA4nObNjIE9uWnbloWcAU2KcUrAKmArG5tuSekEcMCDG2eEyjpBHP2q6QykYrReeN472M5gjEEouR+mZzBAP3KAHXlrCjgducQO3soTeYAB2hNUjG1heXsn1hQr1plTR04BMcsaIUI8wGY8Q6glExEoyvuxM0Rd5nDl1GieOHQe7Cu97/wdw8vSZxPkJcl1y8jAQoLmsWkLEqhK08j4LwdeGS0xgB+T0VLcg5pbMmqKX0nUg5YDNLzClSL89rSjTmh4HsxsLTFWTI12WEmLsM7XVX8pE1iLapLHk/krJ6UCFAMprUEoqhtGTif35zGghcUbL+1M1qS0bLfPEnl9O3gZp6oVRnk1a+ssTyIRCKnXBwJp3ODkYYEk3+OZ9iOWiZNXVKp9JoS49TRtR8xhl5I45hTyZM42CGOgP0D99EptVD9vOgwdLYPLFkziFJuY3q6MuHQdIcazJpEYI+cTryMqwnEOlCQiDwQC+10Nk4OTJM3jooYel3IcmJNRNSGviPaX9oTYa7wgX3ni94MaFvExDttG5pBKLrWnjnocsbZZKQEtrccX/siGiW+d1dpulHh6k2hoeumJi7dIjOhM2hmJqrJkBFvqD+ks66mwaT6k7uPSBNZsLOv+cd6XCi/L6J9iRMAUbXhZwd2pzcmw7Nwrg6CM7k2nr5h3ZVUxhhq3AJZGJpOvFgJP9Pk70+uhHqSEkW9a6toH+U3uH03YweSXlNzkFVK3QMTlzkPQrxLOncWt9DaP1DYx9H2NyCOQl9JFirIsRjjSMwURYXV3F2uo6pPYsJe2IdLKOnMRAvUfV76NX9TSZH/jghz6EJP1ixHi8j3oyRt1IVQJyiiTM4EbKhlx44wJG+/uthU3wnpLy7VWdt3/dxmurp0mGmUHP/Hc4uXknIZap3NbC5nNGFFTOsDtfHRkBccFSmhyYZhbccTjm8ViMMwdQKBXfTrLF4EOLIbSQOE0tMGdCO5hdTGIOgDvTUSbHxaQzYGf1QTFgSBGnlwZYIw8fU3hY9fbMIJJdQHnkWUpQh8GYVBWkT/YhedDKKnhtFdWxY9jv9TByHsFXrb13ZTLaPPXeNK5jx0/g/gceQFV5VJqI4JST6grBaSEvybX1ymiABx58CBsbGwmXmhAwqWvUkwmautGqBII4ltwemgbbW9uSJJIfkeY9U0rZ1NIOibYqVqxIMe8c2TYvZWKTZttzXuNFTp9ZJsKdNku+SPqBeshb4z+E0yl32P1sqqJ9bM8530P5+uSHKLoo8XZOO8DmVOcDRzBpXVYiLawsJfYTx0hAlbzWxJ0J6mWU66KDVjpwxUKFmdIwICLGBkNinBkuYRVSO8czAUEIrEEQ4UEMkAOik5zPwHAhwjURLsiBumkngGYxeRm4QIsI7Aje9dDvDUHLy7gM4I29ETbB4Mqn9MWyzTPnjSi893jwwYdQDSpQRZIRVPUQXSUbvr1Pe/xcr5KYJQNN3WBpuIT3f/BDGDUBgQkhMppGHEt1PcFoUiM2jMBBzm7xhKqq8PQzz2gyvY1FyE/mb6zLFH/zhM9S12dkY7U+q7po5e/TXslG1cW2urnIO3tbBLOosQzbEcM5tlmnF7WHP0ODmJ6t8NmAoE4gxLa6bmwJBQGm96rOpvCKOgC15P7CZx/OIQSA2Y4FAmTzdKE0FPHEtklWauJI69rljFxe3vpevGHgiCOVx8n+AMss5U5gCx+t7/zgvF3Hsl641b0gaT7GSGkz9+GkSl7NjJtNg8v7u9hH3vKzqChTqTYSJHXx1Nkz6PX7UrHAkZbCdLqdzWlRrwrDpUERe4sg5/Ge930nltfWRVozp9pMQBSPbWS1rVQCOsLNmzewu7tTKFaq0mtAvVQ6C+ULswkU6XltG7LjGlSgU+HVTHffBuEt0qQO1QhIiR7tnovX7feZe9FtZVmTRmk5TtFFuk/x1G6kg2XnbWyyy8lxSNy3Le9KFcaGU1pokTv3mMTj9hALhScpUt4BG4M+Vp0Dk5ZMoZA8hDmelJUuM8pblRyIk+fXqv25rq3FgCRXRNQEbNY1ro4nqF0FUA+Ani5mSNlCPrnP1JwIYHl1VdPsJCOoB0KlsUcHkjNQvFRBiJwGBzDjzJmzuO/eBwCmVMBLnC4GGRZihxNmxBH1ZIwbN28k51tWMWMBf4+STNtwLxbIYFpuUFQI59XJKuzUvTPbNJHMd8rcHrFn9tkRAIQpm7pUodtsZ/6wDe8J7ahEV/OTcWa9ithEWwnDhU87IENI/8+e12CzKro1VdaqpugQzP4jfYjdY/aRfkck+xen7Vn9zk5q4Igl53BybRUrrODgAr2YZGN2qUYpcyC2MJyqjw6ovETrZPgWs8sEJcnzEsNpnMeV0Q5uhBrBayAeKpOYYIhuICWSvBzbazro91X4kFZyMMhK0rojD+89er0KMQSFhXh2V1dX8cADD0hSuznZYhRiJT2/xpZBvbrjyQRXL19pqVYpdCK8EIHV5iaXHBaLMYHSmlhMq62PUOdValRtaTKPcJlZY4MHq7nzf592BBme3KEslufp/yUjazsxXb6upTnm5ltOqmnh1m2HjnMSi2mRtZYuz2WUC0VWN4hMHVSOo32xIn9gcT1LIWhZ+NSz6GmQ7VQMT4zVyuPscAnL7NBToiM257hTgs/Dk6eqG11/yzs1SKQnoqTwcoenq60QiTDmiKujfewyI5IFEAypU+GSAmlyqRYih8pX6sHOdxnOEjn0K9lILWoTJ7+Ddx7vff97MRguJWkVWexMq3/DMSiLdHDsEGPAzu4OODTZTs78sDVHhoaeiObRTLHW7VfuhzrXzuihJFJuE+zb0dL8jKHx7JHl0N4hmsEovWw+WW1I5UkMD4tbC46WRfmdqrUmRVh7J9JKBMjbwCigMEeES8k9rpi0xURzloQULSaxuxy1xppI3eUj6+URAVVscKzfx6n+EIOIbH8SITogOiE4dg4RhEASw4uOEcmqKWjZTAAWC2NjPGx+SNUImAGOqMnjRgy4PBmjIc2O4gDJbzUAGLJHceAoBGMIWFtbF9LSSeaQCiXHUc+JbcgFJYUYcO999+HoseNo0tmPkh0UwgQxTBCiHC0oRbYk9LW7vYV6MtYNZOqbNmYAcZjIDCnpPR1FEBmTppvpS4k9F97fKTzqSEJyOvNZntmOF7fbT9Q5Bu4W2kLrPtJVzIE3B+ZZDq48n1nvrTndnGdCR9ayyFJLQoU0aMBFb4U24SgRFUeGnpc5sx0Q58wJ0QlfUAReVVW039oKZTmwtq4NZOWDtU6QqZ9AKVHkSlZdX2zWgMoRTq0sYd1X8MU+PeNm5LI6I8yiqHoGVU7U82bZGgmwiTDzcG39G2JcGe3iZlOjcRXy6WidebE9RcbQxIiTp8+Il67sUNVE1kJko/2RFkdDfr5Kz4989GOIEKbCEKKta0mMjyGAQ0x7DZkjrl+/jtFov1gne2TbcZcyY1CiULEIM1p5HRXzKZ05M0NjC/q0JJFDybCOaVT2n55RLCEXO0tmK8KdORXqeBkTLrOI2hDI87anpCygYjCkIjw/Z96IpB2QIcStQtImQTPRCuFHpym+SdQLoSRpiBxLE2SReKVJqawmWczI7pnBvVkIawjCieEK+oDsglCp5xjiweWYSn2YRIfmOCYuzgIq3c+dvafIm2UB9UCSEF7DEZf3drFDQHS5dCgXI27p1Loca2urYtuaypqaIAAzsLc3QoyMvLVdeH+MjHe+8zFUgyV5FnswS+ZRCBK2iCznhYIl8X53Zwd1XafxlCxyVmuHNGALB+78hvxtel/S3CJbcVZYBRCkTiVuZlw/FRdNlSHm25GpbyAdUjz1gDtqeW2k5dhwK22VOpfDeFh74/siJnigWis4m4GQOUebUyVVFFqpjyltNjU1XDqJEF1YY6HO3PulGpCmC+M09lwCwbPsXlnv9XCkP0AvslTvi1lyudLGIPWgxgSh7FHmMs1LuImlCUo5zbzfT1Rnjxtc48pkjAZeiMUqCpbjTfQpTGd9Yx09tTsTq2JjV+KA2h+N5LQyigkeNs7llVV84IMfAqOSTe5szA2AxYk1m4sZCCFgb29fD+LFQiTowjsvVlsrmHmHMb7uL4dw6pStzG+d1c+s6xbPyBhrnspbcQqV+bb6jT7FWHh3nuWTuKCDqRnOfeYB3tpYasvzp2ZqZVItkT2nhkRpzQlWAV5sCJlWyxowYmh9LlRh/ddnxunBECeqHqqoOyxiTHLCEpE5bVJs28EwL3Qi1JLzFbpfAYVIhImrcHkywpXYYN85RPIoVQwbX2JZDBw5clRPKkPhsc1MzpFDDHIYUAKpzZUjBoMe3vPe92JtdQXcRLiYi0dTBBBsp4QxPeDixQvKIIpY8IImUtJWgrq/zLha3WIHYPzdyPxp9Tej/27L0l+Y9d158kEjKQVMIWFL5oIsQLjQrma1xcTp8s+zgJuXjGcuEDOrtNJBJKQUdZCUQDLxy69ySxuBW+MCIXJARI1lYpweVFjhINKTzWYrmEKWvcljm7yWzGB0kp9J1VuSar1JKdQxOSZMYsTFvV1sI6Dxkr8bOB9vb1Y/MyNwhO9VcD0tcZL613o2zsF7j/3RGPv7I1jtIAvZBIi9ffr0GZw+c0aO5YtCoDJNjbuGBlb9nkB48+JFqYvLyJmN3TXSf5pOnzSEoCZAUs3T35YBi2zQtu3N8nWYmOXtZAfNs21baYnkksNPBMGs8d9+E0womB1RcjLOhDDn60y7MtON47S8LduBDqGpwRXANsO8q14nouMs7bpd5rno4uidqumlfsp/9nzjhhwDwA1Weh6nl2T3CqvXUh5iFFiOL6uTACVpEzsZ0OUz04hb9EvYjxGX9/awEyMi+ez5RTFvMibFCI2EPBKCFA4G5wh1PUbdTBIfS5aATmFtbR3f+aEPoT8cpGlFjmiiFP0SB5cc+U4M3LhxU5Ms0sznEmh+T8occsx3JmKz6AhvRU28261F6KwqN5Ly9pYJ01rCcyqwpI0ec1Rd+UU2b3QzrqbbAcSJtqdv6mfzg97OxFsKn2mwCXOSrXiItWMN6HgAG8MlrFc9VLotK0ZlIskjaw8yjuvS31JlngUsSxOkVHFMPaZg3GomuDYeY+Q8OIGzM3hFjEk9yZZKS6MRZlbXDaLuzSx+SbvlfVXhnY89jvsffEjtXxIkDFKVD1HKxXBswJAqfnu7uxnGCxpBpSvrRvaU/zkPfYpFW7SdpbzjNglynlQsv5vXLER051bm/Ba5zZSMwYKV5Iwht9irjhltClgkOw+uhNDtrvS4EcnOfBXZ88DQqnxg3K2w8bItKTZdPkCmLCuoNhXnCREUL1gqC5wYDrFMWhxSS1WIGpkT9E3XN1XXkVROzyqHqYmWomcjs3nk3FYQI4BwfbyLTa4RXLIoSqc1AFFtm9iAfLa1AdvxKqMJdY3QNGpEFvaJqtbMjOXlVXz4wx9GNeirSqUMyLSGUCOGBuCIvnd4+ZVXZCwsWUHl+sVUypRh9aBAIT0faWRdojBcsPezbNIZeNDyCLfV4amm62rr2PYLJL1Bxp/+Fs9KwO9oRFOmmdbgYMnjjnPGk3GixAjkekt52DBmiwKvS0Y8pZHNaYuJM+NzJqqiX2YgxLzAs/cJ2rWZ0IQ7lypHWmV16JTSU6dG5VShdptUPycGPEdsOI/7BktYjfJdJEL0stske5wYVpAsKsHbHtEYYvFqRG0uGEvLj0MEoEJkh0kkXNrfw46DnG7mDKnzfSE0iA0nfp6n55I63AQ9C5PNssnIb6oxE/DOdz2Jd737PXoKGeUSIcxywnSsFUwR58+/kiolzMIDyUjK6jgr87IPXWHXDackNnkYb/Ac+3LmrWzSB4VeTcUiyHOZbW95e4KLrbmZo0OGwrwrCgFE3TlkGGRPe2Zidn/ux66dD7cDJCcVHZa6lkpK0mwWUw8XNuF0eVNq1PtkUnZCmSyI1lzRfFep76qeXc7cU5BW+vNayuTocAnH+l5LJMekr9l9sVR1WQxz4YjFiWm20AykkoYgRC7OhineM3lsh4DrzQRjb1WCBH62ACEEmKPGDnMSMGpVeufADpjUdV7N3IVyeEbDhOWVNTzyzsfR7w9EwjhCJDtMipNfIEbGzs4u9nf3Z9qbbGNxXjQg58FOCk6zlksk8/62mDIXEjNnR02teIcQnXNavT7vhoFqBDlNJGMaF6Mm2zivxBgjgaMRqwOh0OAIabucUvc8pMxgNnyeCnUU12CasbR/B2AmkEnYJGmzNkc6wLe02TrhaFI/y65MLfSKcAt7Ku7Or5lcNOXBQu0yTu8LhxeSemAmD4nX1YFxbHkZ645QcQMfVUpHZPXPiDQhmACVi5ECmhOsrI/tb5IVpDgpfdVg3BiNcKuJCOSThDT1qmlqTVNkeO9yFQN9Nlj2nO7tSA2gZMcUI7IWY0S/6sE5Suq+lEgSbSDERuKlscFobwe3bt0sgDdrVbhgCKZmx4RgLY1l4doubmmtE6PM9zNLplT5e6k6pj6QZVx67pSaXVwMTEm5mWNTGCycBduKpv1ZC1vJH/Kh01AH4MFp7QuLSmdk5bR2JSGWwztosCJZs+rBzMrdZunfDkQm/KcnQZRGhSSRATATeojY8BXuWVpB2NnCDkU54o/Z1hHZUM9nhUjSPGd1tKCbTLgiUQLFtEE5pp6A3SDe29XVdVTSI0ARcrq2FKEmx3IoEUmeMlnigBS8xc2bt5LaQ3myCWYMQdbBcAhyTgmSxBnUBFQEONdD5Amicxjt72FnawsaU2rDEVE5mz7RWTpiyRjKdUH6bVo1nV7/uQ4gknBUqlEFOeNGTubKMjWtfEe9dSVTs0s075hZHDKuZDZMCZ+mWqExY/YVrfnIvmbzO8yfszFY6DpP9a34s+iBC4nTUrkdq5PDFUChnBIGEtXQzVmMksBaNovihjF1c34A2WlkwEveTc5kJd/r7hcuFCCOWO/3cHywhMn+CLUzwsxSr0ALHY2qWORyrLQ4KyPlEat3xYgk8xdGIGAnNrg5HmF5MEAF4fKV89KfppEJF83yKCpcIjP29veNI2DWykk6H3DPPfdieWUFO9u30HOiRlMEGgCIEwAVnHdoAmO8v1/AvdR9bC4yrxa5UVrdDCkqf54m0KmxzvidEvySzE7wJFacKAvLmYBUnIPa3aTfGSPL2qtQaGA7EGu+hGqHhooUF85MugWF5JySX9z08iQJXM6civ9LpV0HMXd8C4nTZF13MuY2ToWL+GAx311MQ7/sZTOI54En1Y6glJCv1WIouT9DJt0Q3I8Rx5eG2K0jRrFB09EQpxRGEyCFbE20CCSp2s1SMqYh+bmMMRwu1xMMexWOVn30IwEIupdPdrI0TSzGTgj1BOPRPvb3drG6uoaZq16Ol4Hl1XW8451P4sIbFwFM4GVrBFhPr6oxAnNEE8d48+IFPD5+Cl5PSMt95Zgsg1LVvpJsStjMI8bbjlkaz1WmTgQQ6+EemhtMxKLmMuWYL2e/v+fUlUhF5WUydofIWoDsgLHJnMw3oSpy4ZU1eCQm1tm/Y2p0Kg7NJV7bXlLtxXwlZiZF2045uy3OECJKhaIMS7NiiiTnsjRrO1TKa6dty+KKDjOxtDsHcUTKX077SXPWTweBHIO1no3jiD4xTqwsYc0qmxPAFHXcVm6klCRSvLpl5OjvU0U+yYEo5m9Y8omZCHsx4o29PdyIDQIA5ypcuXYZo/0d7O/tYDzeRVOP0NQTNON9jEZ72NvfwXg8xpmz97RU2ak1Maj7Ch/97u8FcQ+T8QTNZISmHqudGBHDGPVkhMl4H7e2bqFumtRH8poqtHMaWYAUnJa6QLkM2m0S3yGbbeeyY1cp1edBgrYgr2hG0cYcDdcMB6yOkYzUNjUvluvFOEwN4ixs5PFlGM80mSw4Eh10mRZLnyksrteTPocjxIyZUdmybAuJ0xUpWKWkse+tY4LV5THP5+xnGrdM9WbQ5ro5qYHaBJL0x06cS2nLtoCZJJDCjZIls145nBz0MYyAlcOUvgrCsmY42jr2TasGpEoLWbaKQDcJ+v9v78t6LbuO876qtc8demKr2U1KtCTDskxagyXZlmEgeQgQIIjzj5OXPCQIkDiWA8eyZZhWZEskxam72cOdzjlrVR6qvqq1T0+E8hA99CbYffueffZeQw1fjctqjADO+h6fX5zjXABrip/+7U9xdvYEnz+4j4cPHmB7eYm+u8LV9gJX2wtcXl3hxs2b+MqdrwDUZPPaTbDU4GGV69dv4ve+9Xtc/fDQute594Hdbovet2gtvKQrknJiY0eHdLDMUII648tS+Ze+qkWLsNAAIXiUKZAaOci+2jPCdU07i82YwZQM8RKn67OjYa53vQ0ZhUDNn5lvANFShAUPIT4HGRcZnoJltbW/Kazle3JAk3u4gvrBoCsYYHiR9OchrmZM3PY4pJHqh0Ea0MXDI+n2sYBfWQZWp1sTGifp0XEAL4R9+/QUF7uBj3eXOBe3K9lUYhIH4LFsIzSzWI62XADmhxhqaPDBomVfKAgMPXoTfXF1jtMmuNZO8PT8DMtmg9Fdc+/3O+x2W+z2e2zjqIZv/d63cO/uXZSOO7gMk+NCoWp497338Iuf/w1A7+pQiC6wYdjv3H+t1D65P/5vNgajUFJpWXBewhBIisdvAGGfc/k+ApK5payinWyO6JbgBRM9YbaKRmsYYq+A5qk9JN/xJQdT966iB8JhrMKEM4297A0yPXcEOrQIH1LSvMocfLm39qWOifrz2dS3579Spk9rWJIab35vFhQYExbK9e6mwfDfpxavl0hQm8T4Nxi4d3qC8/0W2zHQw360kIyU3CNH7oIjaSWaMzFxwh9fxUL+3VSpUPihswOCJ/srfPjhA2yWI4zNETxEAdjo2O322PW9F0Vrw/e+/3201rBnZUpqhyIU/t7rVYG33n7LtaJFl4cx/OyUMTwWiOh6kGOe15q/j/lFTHPevAkgoZj1YF9f4pXlgGfmGyTKQ7LBZO7M47BQlZgQlfi+KGPtKDRzqLVWQ3rm36UJqSYpPECFcPAsw4sgZxBA0K8/7vDbBz++RNi9gjn3PvxQ+3PtJvEywxq+idOizJqU/C1U77GcHNio5xiJaASzCHVcbAKN6QluawgQz3aJd4f7XGHAGLitgnH9FOPsDF+MAUjLliZsQEabRmI+IDxPBB3CaLj0sxgzG2vlypsGHQkuLi/w/s/+HnZ1ATWPPdp+j51tsR+G3a5ju7vCrRtv4o3bd7DbD0B0JpkUARxDNcEa2G0vIx/Z7bL9fo+BHkTnRdlMsbScg6zQxgzd+DtujztJbfXJl4a5UwgkLZO4RhLFeIa5NcbamXQCz6wy88OuED2PI1sylLwLZBuetQahmVPPfoYxZzhq0x5SpsSfpZ78BdnnN1j0ENaWpoz5ANM4Yv0jAeFlIdgvd7L1ofbMBXvelKfbpinwFGzmgch6BXwysGROn+TaY5wSlW+1tdOJcbsx1nBBbGCB4c6m4er4GrZn57hsXDCZvi/TvKbLacjftSKyF2y8Ff58/Pgx7n/yEWR3hWZ7qO2w3V2i296bRG/32G93+O4P/gg3vnIn8hoC9q10HFJg8F19GD7++GOMAbRDgqKCUcGtGzexLC1RAVI8hpYI6Mowx8Sy08/TcqRC/BJcGlBxSB3C5Fw/UjMJjcgcfaTlZc7ThOcxsI+wyoBi4RgNUYIVgllRiOrFJHow1Gfnk2Vz6XepLnsvfKzVX8lBFPxzaxEpIfm86xWas2AVxKZgqkwbXRLWbKKcaeiWd0t9dxpjTWYdOy0JZbmRqxWIxzDOyYGURVk6x2BoA7i9OcajZYvd6OgTo9t0fyIACoz0CEyOMaOwHjArGc0hGwZgA9vdFbZXV9iYAYNd+tyhY30Ao6N3ww//5MfQ5jZpEqMUG83BeTKSquCjDz4Aj1tf8VMgh9YW3HzjJlrjgUwODSXXKzYviAVwya+59lKTArugvTjGuf59iDpCvNhHGKr4PhaT4zHJUeaf62tGZyMRQc4BtWeSL5qmAEyMfvDkDA3W+Nc9jWuBV5SYvpjpXNCgz5k2SJ+pi21+9rPXy5kz4KZC0CetYrJiI6wW8ZlfSTpLxhhpS66mJwAzKpjJ6sKRWyRYvS50KscjNutxRY8N0WQ0Sq2B6wDePj3B+dkZdmN487M8V4Rd4OmVLQ1iwo51mp7qbqMyT4x4IOzY2NTeO/a9Q8feQwV0nw9jtxa8cesN3Lp12xEGmUz4fj+YqTpJhERWBfrAwwf3wR5EmaYIgM16r51ew9e+9jUUiZDBuAcpBZGNsJLOK0tpQn94OUlNpDAJd6EXjZRjtaE2ppBNi/GMiT2fZ5elQHe0RLgOm2ROalwOZNZlz16VlIEScGBR5KyU4v40LyaEMymwkBrI6KzV0hb/v3gtX5mEEGNEQ8CN+QamaqQ0rI1e3RZ/JkByTnlmi10QlMZk3VwShCTpw8KxApmExsQgXJt8X7ytycBXlg0uTq7haneFi0kjrSTkgcSctm31Wx3UNlprRsYSwHbeGc8i1tR7j+T7eK8c4Ud/+mc44qnZYd+DHnFruXLzWhgMfb/F0ydPIRKH2aba9pmPobj1xm3cu/f2JDTXHlqutU3+g1qtaWUSy6b6SR2yzj46JHA3aphYUaQ7Ueo0L+kSGkw8UwtVdPO8i+ufj1JNkTYANNKsHczLajfnvaUA1BBaYuplDNNYyV1Vs8ln0nFnGYv18I6i7gxmlg4zrWc+53olc/rejHKemTcgliSYkFYIOymZdURCgCbDmAI6SnL1iUhURq2cq4Fwmee8k2xmWO0ahUQhqHxiAaSVsIxItwFYBLh7coyno+Pj/Ra9eVyUzavd6WIwCVcTay9NncbCTlApCEw3l69DENgQ7M4u0KK/bbfu5WihPHvf4+5Xfxd//q//DbRtIj9UMg2WrvZJVichGYCnF2fotg9h4DmfaaPJgoEFf/SDP8Xx6Q10IUu4nROrh4SaKZBKWOXC81/0jHMliVjme/JRhLQ8Y8cSgjIVMh1B6s9QenFj//mxzGvM78Z4KHiFHn6OxCwahU+uOpvX0utZB1iimATm/56S8D2GPWUqzWuWY0IwLKYxSUYYDAi+iQWIfrcvM9tfypzb7dNIOFB0RYQm/GSsZZLwqgpYmzJuDEP2sdED9F76pkTskLMIxNHC45nmnblDoABnbYjAu5unjQiGZua0K8ZODTCFRlf2YQrBHicqeOf0CGdPL3ExBnYwdGkezxRDs1HG+xjucMkhT4GgIGgZhoHu5Vep5QbOHz8GdjsM26OPHfa2j3aWPvFvffsP8JU376EzOUdGNRYgXD0EJAIAAxfnZ2hNYb1hYB+M0pOolrbgez/4Eby/rqXXlfPKGDJnQ60sBgmsNL1w0qlT+qTZxADTIgmJuCeD8hYPu9LTWU4/D1cxf9VXUYMYZq/5ZF5mWMkZyhlst9tBl1bhsPiepG3tv4u24zFHJTl66Ex6jjcFwkFYxKBpJZRnwOKQLNSceX/nZg5flwNhcXi9lDk//+yjCGBLEaEJoBssTaI+zxO727JAJTMMMcQmu/vZc7lEgCqrJuHYM/e3gFpDxBPvRdBE0JYauljLZ9JeBcTzTQUQ5uSaQVsJhWuL4t61I3xxdYmdGbYw7EfYknvXchRI3n1+rtJExEl9s71syyAy0sXfd3uM83O0Tjg7ohi6Y3SD6YKvffVtKDxpfmgIs3pBaPPgzrRnHMn04eeq9O6lcGCowQxAw803buGNW7e8671TPETEBWFQO5tMGQTW4uwWOPyH4Zk4XKVjxM7FvumUJ8pKG9A+z/2mVopDmUPrJuqI2LCTuTikjHhu6nkyqRXSlqgo4rjG6OEU5LvF93GeCg1T0iyVApiNpPlvnqfaMUr4CyDWYVQSITC9p9aIMeXLfI10epFq7BleeL2UOffbJ9NcnOJ835fwdMZL4/h0k6kd8lTRgbkN/lRwOuoRLkyMEpeTELc3Ap6EGE1njSdbpHwFoBgycnMZoCZEpBoSXSAQDF1wZVvY2EHRsBFAu6Hvdxj7HWzfw1yKYxKyjC1iWQEnW6CBXY/jAkLrGgzt/AzoO++q0Hsw5x7DFMuywc03TvHo/iewZVMaIY6o8MUgkcichIp9U2y3W5fUw2Ezoouga/aB3/3mN9D7zitT+j7lve33LiwwIlnBs57a0RGWpi78UBlYSNvI/GzW8ISKKLR5BhIdUS7MSxuJHDldiAEtNDg6xn7nG6stvaRqkrYaGMJ4zkU0JVolexCDSQcEUOwgXZGHATuh5M4NIyS3TJBHkSI0BKFEJpmF1qTgwkRPgn1WFTlVxnrEEZMmjGX6nvjFrGJ90RQBvNLmzO3M31WercNdH5SraFgL8+PQclmQLBeJDQClrsMI9tSRLCnk/TVxKoXqZtCzBtHvE5iOVVTBaTrgswmGDOzNA9l9ADvbObOgRU6wocH/38CgWNDgHmfvhEBnDkV3SI8INcX2OPPbwPHuDOfjEvvu7+m9R5c84Pj4CIsYvvj8I3Ruu7ptLvQgK08wM6AXjG6bDc6fPI4m1EiNQ5ipR4q33r6HTz/9wLv6RZG5mymlhTGm4wx1ccLss8BD2V8UdZMALeRAJkE9GwCkQbD4e5u6nQ4/WxQQqDZI4/mhcE0+AiJa9eYd8UYVAVocByLhLBMqDwpMwISw3CCLn+IWpmx6WMkiVBY+fKtTwUXQiBptnqATqjZxOuwGk6nX0QgjLKStiKCboaND0ACeacOMLHzjkPWSa154UWqURcCfrTL5M2nYkFlD8e2SRz20J2CyhydpA2LOtO57LQnmH/r9PapElEwWUlXBPFuHMHmyNaqIl0P0J42AIgMi3YMxImijh51jwHALJIGeGAQ7NCxhA8enwo0Lb6DsgyhC2psT4+XVJXYXjzEQHfFYdWEGs4Fbt26k566BvYw8E8ZG83UKO4U2mMM5r63sl+cYO++0VwznmuHG6Slu3DzFxdVjiPXMgtIkYlvbawZIaFc2vc5EhskjmluEzI9JQcw1pYOIokrEtaTtPWd6WAgoMoI0Xz+WcTi3xHqv4+JCLS6OaIze5zGSPoZxnqGjDkvwBhmHnlM62oYzI2GqwWkgQ006ZfaIK4ZgtDLh2G1jZFiswX0m+xBm3gTcsjgD+P6zzIdXMOdceRJ6rT4kfJLaLaIyH1P+C4IeECMRemzMyMGadbiHViMuTvd15YNSPAjMj3EIDS0yfNNigQlLBl3y0mMCTsQNbssavNMcbb0OhgVckhe82UNhaKJe7AvORbwFT8ackNoLUOy3Zxh9F9DR/HSs3nP+b731Fhgs12SYOK6d6e8SSQ5iYHqFoKHZDtYvAdsCkfDAfVJR3L17Dzdv3ADMO+GDdhDcniK7dTqQHE+DwsfM0MgrkyYU7oe/yBkewOgTXAkvqtQt8QYeGwHUaQLloR2ZGodkDH9g5IqJwIvhaVw0NMac4es4zKAYHqaI9zuPl+B3BORN3hQWDiDSaoiZYKw5jilYwlxxR5B1W33ue1+HJwtYeufUQkQphmgIrgF/n3+9vBNCGr/hOZPSoTHPqXSH0CetTi67y9DYJLUK65KmhRuY2e7R9iqgKoS7NRMJkLAKIF4JYhrByOr1m1yReJakYEEccqOAdZgaJG0nztsXuYXAMQ04RAFEp5NqMpq/znB++RTd9rCxh4TNyTUyCO7eexNIQEtC5Kczo1PCshY1Ip9jB7VKcmPaoqjizbv3cHS8AYIhNAgnU1pJAEJxicmZJNN/QLJ9IA/udnYSzDFqQUBqu2TM2AO4Y4QmAikEiQgqna30DT/jdzSUQOQQi0HVkQ+jzUxOKKUR81Jn7rSbzSpVclaws6yZaM6dUiNMurH+TkxUzBEP+zBxN1XgUNa43wMDL75eniEUL/Yi+4IJTEz29faf6NV1VgwznbPLSWhowtVbYuKV40lpQ2mXxjYlnEwJDfHeYaWrmQQgkRpg4jE0h7he0sWVVPXxmSgWVaCF3yUzf4Bmkm55h3xBHFoCmTBRzEuvBoDtfh9F4g5nswIewPHxBjdu3ihbx5IvXJihOzEmRKz1RezEsN1aYIUAhW5w/foNLK2hRwf4NMRDgnfzomRoOM5MXICAWxcxXzNHEPTSKrWWQJXuNhJ/BzVcxIW4S/5Mie+K+Hpw/0PbegIHAytESS3RgK9VQ1K0UQFICVPSps2pgBXbJV0qFEMshBvjj9zjkc9J1MWxc/eHVwEhPMGVxBHjUEc8kVqWTCkq2ZzclVLt3+H1cptTSvLkua0iWYok8JCJMlgfi+qeTU4YSG8tYWJIXCf0YkRTJ37BnqrUCTrhDTc+IIgs9b5RhMIXz02dMk5uFKgxVlDOStK5M7SGNA1E0H3xTdnMMjQL3eO2cqFAzNAvrtCGH9q7NfeqktHevvcWbl27QdcRPOalMUuDwc//VHD+kgLQPYcNTY+cCAm1gglPT6/j7r03PayTHtxwU7HfUtrojFOWiSJOvZkMQWYlWhlGYdwyRCEaNr2woRU76pOkSbyMb7JWVvjGlUfc37gpSDkxp6nDdJ9HJVLkobYwjwSQbIRMjxACQQT0tBsFon/OCh8VPs3C5OEzp056SUfkTF9rIghvNhYJK1xdcSY1s7l/9zPXq7vvUSNMhMchlRT3G2Yvq8x3i2SGz5wdIhiT16xl3WG30sjcPuQmSj6c2rHurDFV9z72CahxOtOFQyS+z/iUS7qwyWx6vxZamDUYHTX+aMlRiAH7qy1YdznmNTPg1s2bWFpLGE0G8E0sLe3HRVi+NSaHfe/47PPP/X0i0LY4orCB49MT3Lv3Fgy7WP9I7+Mz8gxScWFpKJMAJdHTnxkS39lJ3F63YM5YIdFIFObeZsy7/AWzYPW2nrmjLpSUluhMaXTRCLJap8E7DoZQ5cB9bkEP5k5GJdKS3DHYJF4zfMdjHM1ZtrbZY9cu9youmXHiXDBKsqjBkgZRC8KjOcL1VUAYE33x9Wpv7UEWA8EGmSuNTlmzUUpDCDILI7yJgoZhgrkHDwdtvr7c8liksMoiDoZARuTTnmlvwRqFlPJyl4IExNZcsGSYfGBkM3H0IdrWTi4SZ63KXOlAzyhGL0RpSFjb2oKbt25haYJhRcQCCrGAQoZVWwuZKlXOz87xy1/+EoNHHopC1B1jJyfH2GwWrx2N4wmFNneCAWe2kdqnEAHHQ+YBUQQZKYWjM49Pl0kFWbowzQr5TAnGFgpyYQyRti9J2D+m9kovgk7FETO8FQNhdeGxZ1PW3f/RIXTqxJRJbeu4uYUQEOgY2AftIoc+qYNSpaDpoIEGAYX1cPRFojDF5Zq71tcrvLW+k6tUO8ILEcBa0PKs1daakwFXyfUOT6gBNmISDWlzjnRtS0w+0uEIeYaUdIQfw04zRYJ3R6coLWc/0yMM6v6fwD2uYMMRUUFW0EGQwCvojggga2LI4fm3M5YGU/Y8Cr6ne/3G6Qnu3rsHtBaaS4MXJca5KY0eBeEANZo/99r1axhjRNyyuadT3LZ6485djDGwTIjHci2Q6wupcIib3lYTtDJX2NwqnSCxtl4ATVqJ0A88+C/ca1XwpqIMPq/QWf4udimZFwgtU0KD9AZlOIjCdXjYanraDNdJMwpkih0UUNTalJZH/m4MgUmLveG+C9jDdkLiocHNQy5mTqtDAV2CRidtac4fL7pe4RCaciJjpKKePmfBMBVtKU8bdeF6SbgyDk+aSMDNIghK2zxJWySFAStOxuyQoL0hI2FZVsYEFBO4DdSCwHuc4ZllbHBngkQ8RJSHBhGmBZiTxTsn1FKAvZQsNafvDsu5zIAxBna7HfZ9n97vk5MT3P7KbUjYlORx/k8drbFWfCMtywHByckRNpsN2rI40+wRLnzBO+/8DjLUBTrRGLfk+4h46q0mE8OkMHUQuM6QKiha7D9S01miAcLcuq9+Tg9BCA7L/7N9zEyL07rwQS7AAabg0ZvKfxhhi9X6uVNQohNDIQWpx3JQ/m9VqIxkSEQPKZjlo8vR5fRMA6BMAj7P4EkZU8joN3UI0cmSLmSZF1UCj3OhannTs1fLX4sw3TsZAl7BMgWE/W+btsMLa5VzDBsIFlZnC8IxyePl6ZCovnPOOH7ilxcVadh1qq6luY0cJJMBoUBjlQuJZ5LIgkg1hPuqd2bo2GC/G9hud+gdENtABHjrq2/j2um1A42C/HfCRhM0tPQ0ino4GyLRxR5QLFH54O9dNg1vvX0PgwH1MFlTJ7FgAQGzZcIULOkx5vn6zyYjmYFB/wJ/ttrptJHT8Ue4egjfpj0BUPrkEO7NdllYpDa91eJ0AIPDYzJ/2uluphjviTebaJ0Hw/2GBg953viwQfHpo4pkeCYVLKowozux6LjDC/sprBhHHMylFdrMB8rv4Hp5mxKbNoj2csKelIv+8uaTogbzhZm8caF1CwqGBLeAtrnx/O5aA2t0NU8GljLL3HHSysEQH0SrH7AP7sjWGApIj0ySkrTZwAs8niFIy1xQCSXDHJaI54fejREP99AOz7V17aEOYxX4+je/gdZaEksut9SRfoQ/HHN6T6nxB3D//kNsdx1ts/h3h+DazVs4PdlA6YIyBeKUbrJIaZ+KcfpORsGZwVlGAMhsEhRqYehkDRlDAPP7sFxX3jF7X0ljh76KNS+vBRj3oqivx2lwvmuJ6Bju8q3NNi5A6f6hlnm2sRxxP6MNPocqeUAqlGYteKHCYwizi660zDRPQRf8ESEWmenvOdfLbU5MX7SAPfCHa0BN90oJIBVgF+HmCcFETizH4t4msMyHzpAxDvx1B4vKfzOEIRynhNMD6hUSYBYQUoOKNS+HUmDTFJZ5qcEADFrLtJ5JmHNKoOXY1pKvkvqbAbK7BIYTsJLxpOHOm3dS+6z69obAlzgUN58vM9xyN/35+Tl++cGHsGG4fecr8SzgG9/8BpbNUWTmKHgGzEE9Tfw1IZ8UliHUcs+QooffTOwzaX46eiB1x/yqZ/9Bol6jkOlTFNvGS+icFAunkuDQARXSupRHjHEMrqFkBlS+fY4VUzCk99VSm5JG/XdzqgjpMkXSKsEmD2zSoDkAppoF9y+6Xpm+x9QxDzGGFIZC1GsxtJWZDACNFe/WmU4LWMDHqT+tAYHnnWip8ptWmZGFm5MJEAkvbA8M1x77Mdy2VOL8DmaopE0wr/V0qboTgxUkEmLWpPaYKAkB/4YMsBM+24f45Z5rCRhj+47Ls8fotG+bO29v376FGzdu+IG95mMgETVQMEudOSkedhBqlCDox4/P8MUXT/DJJ5/gra99FW++eQf37r6J9959F5u2wRg70BZzGmOcD+HY4dx6EJwGOIv0s2i3CUT10ErDIoi1GBjwhA7hYiNvL6fRBJW/zEXyedZlEt7eiJOn4AgtOMDkkqBMgp0Rmi6GUIkzQb/ZIIw4aKY7QLC4pkYIE3e/oglXzhGZBh3Nljks9BEdZLEBLzty8OWwVtnkwW0Xhngh3lzKj3ZnvmTAlskSyR66xlDtQVwnNZpngKRW5GLJWrOUNI6KA3FhMLv2ORYjBEzhZhlsHswltHB2UIJLQTad0v3y4aCAsrzXn18owTff8PTpBT784BOICI6OIq9SBr79B9/GJsrDcusNuc6reU4zn7YZy6bh4aNHuNxusd13fPjRr/HFo8dugy5Ljs3Mq3DyubFvbhlwv9wWUnhTL2p0Oq8yKTNabYgalZgnAVnRB0KwFEmXhqrRy3Mkpa3/Oe032c+me1UoeOk5Bl+eFUhmnF0s8RSe47MGKAz5aFIe26Xy/mlGlrlTnj/PZ9LBFMpsQGMskshuNviYNvj8mfv1CuYsybKGHjz1WSbVbEmoyQ9yQGiHItCmzgU2Lf9KUqeyAFv3i6g77dVgQ0OWwR1Ww+ZH5Xd9w+K+EUDIuh/FHiEjGXEgoEyOoIih5kwGYOLHAFJSqygatRsGmig++NWH+OdffYi7d+7g3pu3ses7vHnnHv7w3T90mGg98lTJSLVABFz+s2VjNJJO3+3wj++/j8vLC7D07fHTM/zLrz7AZ/cf4u6bd5kPVAsRnvQxlE+NZRd4MkjtXVZm5HdJA5aEhjHvmRQTUMCG2bPaRwohASB9oqzISyZiWY2cyRIT+Uh34TKC/bJtTIMss0y1SBMUQMtazrQIwcoDrU3Qh0TTAJloapSQ57fFewuNeD1py32xRFSTNZ3fD0YN5+qL9earYO2kck1K7vjJ09P74MJQp9N6dRV2ELA/Jz135cFjmGTejFpcr2d89mJ+r6jnzTIsw20VEUAto14CRKeC2ByDxx1Hj00Pxw3CC6mSzK5wj2AFrGmH+O+8OfUC1no0VXz4wYf4+JOPYXvD6ckpHj96hDt37uL2G7ex61vQoeK6u2LJ8+y5HnTSAIYmDY+fXuKv/uqvsd3tg10aYB1fPH6Ef/z5L/Deu++FXI2whK09nmYGG5EiycOP2U/HUDE6mJfc2OEezFLWhVkReSEAjp+0WQZdjIPQj8pvUq7Tj/FcF2iOTAKtZfJUOFkOogYea46OHVK5ZER/MyxStDiIyhPsfXsVA30idNJw0G2sE+WUrNAHJzTBOiqJaJYtoUFfdOkLPwH3J1ZqSMTR8Mzy5yrOcTALhg0PVTKDRXHvWqhOL6R0tdKWlHfiUGYwm98qMQ+okEk6gLTq+tgh3YthuYPhlQvvL2XrAGKMMVN1IUAkQEcOEyoEUrWIwzD6wMXFOfowPHj4EO///Of451/9C+4/fIDtzrNYOH3LeZdc5zhw8Bsy/9nZOR5+8ch/EzVZog0qC/7nX/4EZ5eXkFbHsJsG8xD5CG10ALBkXh7K46gz3p8ZYoE6JkHhZgPHXWGvdYd4mQim/AnFjfH/jLJok5mzDVejcmfJ1JJ77fauh8dk9sBG+C0TZygMIbX+oSyYnUUgW38fOLgShRXq49xnGL/ikQgvWXjw85jB35Q5m2IiWoONtVxcLRQEfvAPCReZs1mbUevuWickSErqnElIIpZhIavTnTgGLLoK9O6lUoRk1btIVv+LKJq2ZFYRQWuCporWGrQ1qDa0xvuaQ5dFExGotuib5AnTiwo26m09dCKux48f49cff4wmiqs+8PGnn+KLJ4/x4NFjnF1cBWHZtBZxIKGQUObxk7DidyK4/+AzjO5xVNMNdGlobcHxySke3H+In/7vn0HbggHvM2RRQWHBBCw5q8wcg+eDTuRAo33FRP7r6pxg4MTJutkYq7byQJKvqccZ08+F83Db9D+F5oF9ZCvmKAYVuDBuYljE/RHSNFAQ1w9Jn/4sy9+NWGshAhTA8me+J4RcMHpGHHgpuzSUh5tjzllPCmP8xg6hKdVIODFKi/RG5T9Bo4GeNIOnxKXkFfd31YAnhpwdP7qylkJL9mwJQXswwxOg7C57FzAv04k5LKJAc+b2o9s894UOJQvCSGEYfzKIzOySdOfHqtSf/mxVwYOHX+Djjz+DLguse+pX74anZ+e42l3hJo65oiDhlwadvI8JLUKLiAusDz/8CH3fYcNTz7QBoorj41PIMPzX//Lf8KMf/ADXri1Ab3D3RD3Kx6ypsFP7r4T4tBDJfJy/r7L7xrj6vocW96y3tXalVk4zMcBfMyEF3p5Oq8nRk8w+7UNoe4QXPenUCtLTl1Bf8T1Val+qGPGYuohGB4h9we9MT5ybBkQKn7SgPyI+S9CZozWH4jwlzVHcizXnl+ghZLk5UZbm9YpFkbmgpWWJhidIYU4kEq0lBgCxDibWZ9J6TMVjWb7xxgNVR24fDJ5X2/vex6AGbVWQm95es8zcmRfKQ3KS4zc2ekpiIcPUvELclBAindRSQFTw9OkFnj69wub4GjaygWxOcHH2CPcfPMT5+Tlw+zYYCfOxpAoNzYZoPIUieAHCzYpPfv0p9vvuBb0xFp2qNz6//xA//ft/wI9//AOgLbCxm0SYL2TniAPCTiAt3kW/AD3f8fRcRGo9K4FlpJpgunQk8d5aKy9dC3JOtSKZ51redWc0n2Z4koMhzCqryf0WPRCU5pqpCHgane9PaVyfWxkRXiwiEPGUSCyGZSyRYmowiywFGDDCzFDJwnFfqEj7VEmfDb+yUm7hAFtlRxxcr9CcsQAAVJZsOVGSJJxAnBg4ANJv3BOPW6UXiH+SJohJQliXq+KdCXLD4SdXE6vHwlcyBKFPhDpg7ok13wAm1M+yrLSBgYHtIqWCgEVVnFz8P6anWf293e4xsOD45ARHywlau8R2e4Hrp9dwfLSBRB6qoRxjEiEYZWJNPHlk4B2ACvY28OjJU+x2u+LnDljzOFtT77v0k5/8Nb75u1/HW2/f8ZvCzh/ZzNh/NzrLqmYLx7ztppMABxTTj/uUwjIcL/RWu1zN8dccsaLSWdAi1tshZlQXkNinPTIzj6ObgSde27DIl634peTnkecakNQJeADwgg21Ft4LCz9E0FOYRiJeitdUvEWn+UnlMHPPvhms19k8Q/weF66kUY0ssRkd1LxerDdfmSFUi0l4ogI/YI6MkOSF3Bi6t6umUiL732Oj9FCtACKx+gw/LIhBajwDjPCEJGyKbDAtAlbZGnrklnocT4yNCSUFTi2OFUEEMfne6qwAfHmVeYzIPF9LovPvX5xdYFkWtGVB2yxY+hGW5Rjvffe7uHv3nldE8HsHgpOZLPxo/lhE8fDhI/cCW3mPh8Er+vsOJooTafjo00/xt3/7U/yrN/4cN6+fYPQd+q7O/eyEidmRgD2LOKdYmSwqcK+naGixSdV6hlWCR65ULCvJbyJIq/kdAL/40fIeg1NZj3/Tp26DdjNj8ayYDcSUk5kxE99jGffkRrAiZ5jApNSIoKEb646DgkKWjQixJKihug4NaVHLWdd6HDXD51+vPGWsHiRIOzMgRnbdlHmJATZmMhnQdS+PzIctwoxNnYz051l1jrQiBxcBi6SmnN9pbEvpXdAVI9Kmor4kJFq+n7Am1L/zWUjYdB4hNbjf7LFRMc1813KN+6Zpaw5tRLBsFhwdn+L3v/X7OD4+xvbyHPmaKdGBazi4yaAQK6Xz6aef4f7nn0NgaOJa0obBWxR1SFNs+x7XpeEf3/8n7PoVvvud9/DNr7+TG2U2gN7Tg73e9IMf06aktztwjaHa2UrpBMrIgtGHekLmqa32Wee4egqA6VTx8AyrtgyLqXqRufU+vYn7BEcMdCwaIfTUIX6aIyTi26OnsPczQT2E45WII+1VJqN4KGWStMXzB2ua2sufbyO16fOuV2jOhmylL0xZk8DtBUD9xzpUNPuvwFyykcgDWrp1x6ppZGIBCZCd0Jg/yVxTerYkQgi0L0nM6y5xSKePeyLpGQbKniqFqfkeSadDKkRwo5EfZOCGhZsT40tTtOjFuixH6GMLVcXJyWkRQzLmtIv04uUrk8KcAdVwcX6ORQUiw8tBd6HxhsL2HWKK8/NzbK+u8Nknv8bf/ezv8Jf//X/gP/zFv8Of/emPs8MdPeR0l0z4ZzVh1RY9hSU1+khhOul24wG984IBdViPw0mfpmQoZ+ZTojMK2hGJCmasW+X3LHv7thZtNXWpEIqMXFIDwnFIROR2LYvMaafXmLW86GDYiCOdu887fxhtY5kFigTjHi6nFRJbL9Nzr1fk1lI3Ckz9SASuojLmZIo43y0X0ICJURsqDaryZFcbu5IfsTFA2iUON+Iu4ecaKWXe8pGJDF4GxVac06KHc6gsEwST8XOeX2n5PytT2Gza96VsMyPRiXibRIUHsRXQpWHZbHB8eoSL7QV63+PBgwduK4IClKtgAd+CUAZ71oSrJhxn++0Vrp1u8Ifvfhv/6yd/E0kX3qBKbUBtDxvA3ryIu4lgkQVPn2zxn/7jf8Z33v0ebl07wRh71wAIgWvubXQhFxUxon5itjQ/D8qGO180JD/rbIOhVFoI6jW09I4X8NlpS5hr4QWHyErr6ErAOm35PgvIuJmlJGVu+b3VdViy8XdH1YfQcSlx0qAg261E0YSn6wpdTpMt7U/Zz57WEM4GC88unVExWo2DnEMjD1EIj5dQAez/oWQsLbOYEBmjSctJVnsJetpCLEy5qcbvTlkcPvh4vAFjrGU3m3/NF93k3QQYzUMi2mAWx+oJoUIvKcYRHcC1OW22YEZ+uoKbEASMWh/lxvtTGjrZ4IvHX2DZCI42DecXZ3h69gQ2On7xi1/g8ZPHuHFynDA434cYFATGrvA5dgFkYLvd4+TkGH/yxz/CJx9+hMdPHqG1BdCGR48fA9LR4R7rYQMWUBdjYHtxgZ//0//BD7//nZh7dC4wxje9fGnaHTcHJJcgNQq913SaeLGroyzGGQ30tvp7wjVJWYuOahOTBysZQBEFg2tJCciYMVkJRphil9lALsYa7/CeP95ZMT+feOGA4lLTKYbb5JEZ5rFNF/YthLiO6TBpEVgLxTMWjL4HEKEV6bFmPNlbVlpzHb5aX6+wOX1RzCo2k8cExOfsP0PbYn6XBBxjx21qCYQm6sz+mUY4ZeaCrUtobdNxyEoZd4T470Z0AXAoks370QM+zxBKKXUBzLm9wpS93DMJOeMWtiMGf9qI1C4f30jNO6B48OCBJ5gD2O6uYDZwenodV1dXuDy/xPWTo3yHh3GibjDmS+hFxAEzeHfNjs1mwde+dg///i/+LR4/eYKmiqvtHn//Dz/Drz+9j7MrQJYlmUOba/CNCN5//3187zu/70eWwILJypnmP7H+dHaq+PyZFOAEOdE6Ez9CK65qH2MfwqhPLyry2bGpAYmFji6wG7w7odiU3AwZPhkpIJHPdCHLEA371xKdWDCyTVp3GiZpU13plKYdaRK5Ro23UfvmZkr8NQXMQ+uq1HlmpLfVEjznenX3vfj+GCP6wYSzg/ZSesmoSBxsFbRAJscn24nkqdkEn3PpjETWSQY1QrPVeKYYqJW0r2wLQlweu2CQqZUF1Sq7JNDRlI4NMui0Cm0+eyOQvNrBgodgePL4CUeJzbLByfExjpdjiAiePH2K22/chAXYkihZ00wKcMKoVopw76EAy9IAGRi2xb23buPO3VtgvPSdd76KX3/6OX7+y8/w4f3PYQYcbza4eeMGzID95RabTcuUuxRXjFMKsjpd1eEZgnlnBw/iZ2HMxynfn5amBVApfCFodaqInQhTIFlK6GZL0RPfhQyxoYRmSlBZE+u8FwjTIbbT6cihr2bXi7IrhYqtpgrvLQVU8gWC+W31zigvDsTFcY1pneZDKxlKA+aEjcPr1X1rOWj1gK9M2hM53ApMWGiXMSQnCBrzwtYigLUR562Q0SaQYbnt4NkeMwBherqiJDjrAQYaREbWh6pG3mVopJR6RkHhC+RZNHG2lnAjMe9SEIlkGxQLXYnJmdX3e+z3e48td2DTFpwcHeHq/AIP7hsuL66w2219TuqwqQWD0Tk1QhhlVgy1mQKbow12F4KOHdSDWhBdcO/udbzz9Xv44z/+AR48vcTV1Q6XF5c4e/oU26sdBIr33v02jrR5w2O4BxsRS3bUOaVKikQKXUHL1Ba5E5K2J/FUFkBbRmrBGteMUfJecCNoQAmmbPZAOKSvYNLcrxAvttbGqe+VmpGHaIUAiGdUeWBpuezpqxQw8T0qGWJ6kF4LW1DNPBO2yZaiCN9AC+08gLSln3+9nDmbpqBSdbwN0SQiLq7nEtp6cKoeM7KezLksDZLQ0zvNjc4DaNh3e5aIUjCkulq77Wk8vMg3rcHPMunqDgAYMFpsUiyBSzeJ2J6kIBAgm9JnFUz0KlWRTNaPHcq//Zne2NqPRBgYveNkWXCkG+x2ht3VJazvgTGw2+5wdn6OfY/TvigIYNELNsIVQCbcadEqTAyboyOcXDuG7J9m76ClKYAdNriCbLb46hsLhm1g4whmt2B2hKPNdVy7cR0Cnh/q6zf2/tyVM48ILX5HOO8OPe5zLJNEhz1U3jKT2wwDMlwTaqqkeHbmtoV9SiJNYRyHEYP7HcQ+IRw/FpJhtvgjJW6MHfE9k2hDQugu8GobKo6R9jSAgNLMo6JtXkLFJrau7pREBXzIiOw0/0ZmqgVgiUk8y3hxvRzWKo9pW1blY88+cGTlfnajCyxBm9F5rCQcS8pEWrQV8WA3m+O7h1Sy9SYbQOSR9+JAQYxH73FJYgE1GidrQ7ZqtAZFnZuIaXOhQoRYGypwp8QIcrPVx0ityjNIDFhaw++88zs4e3KFp/sd0BYsqti0BYaOi4tzeO+dxYm+AZKE3cNOCokuApWBBi/c9RYuDXZ0hL5p6N0dHjL2LqHHHg0baDPfs+iyoDiCtAWtBYSkvBZAFlSvm1gG+tapRrx3kkNtBjjT06CV+E3m7GYQRL9cXaMiMjwrM5L5QNpmCKLgk0WcEbBoaRoaDUiHCw4oAKDmIwQiY1lqwDX0JmOGJzYRXTFf+jQOtB3TWifC8O+xNCwbkgesnWqgx1g/a75e0eALSX8jHirYU+cH/0nAwEndi5Q0gVUbylGHCrEqXwTRwdtvZ/8fNhNuOh9/Tld7pJxZFUcfuqSFRj3jtEavW+jQSUGXax6ZjJ3PDCjLpIfqxuMy2OLgWRWE4AD+4N1v4ezJGT7//CmW5Sik64DIwBs3b2KzHGOzeAoZ1LXKIh6WsrQ1K4zVTKPW0rCXjnGlCfksErT9AOOGtjQsbYPWNmi6QHSByhLVKwtkad5L1w72IJwYGdwJSaSxQUW47JOE/F72Mk7Y2JIQAUTyeYUxDPVsi7WjGUHd63tAGiIxUujPFQpZb+OacCVBy7lElg6KrFDWykGFYkhUfnPFOi3HmbaiIUvNQJt7hQQQaxZO0zyqcMoBf8H1cuaEZCNlTiCVZnjVqICyEJVrBoAF2gi7pZuf1uVr7wHuzCkNBq8SMk8e8LwAKXe7SLjI54hlbVwWRSMITlg1kKTikEQQDiQyGyrkA0TC/OyUoed0TvwzTF+BQNB7x1v37uKHP/wuHj58isePz7DdXWFZGt68cwdf//o7OD46hi7wkIOU0uYzeAyhxZiaKXgupaBjvzRsNhv0EBWteaJH2/ghsaJe7pYKRQVNw0xRnYRFeMsTnXAMxUQp5GLOtd6HlCL8IjyUA6gOh/2jT1+hljJ+C+mT6GP1ZL5rpQktn+C/j5BXFl1PtJrPMc1nFPmWxi+2rTlOut5jmUzFK1AI5g0bJJvJPVNpFWgQEnH5hMBIwfGi6xWJ7xsf5grGxkLyuWsAvf46ysM1zCJOKVnew0TzmHYkTMMNLTOMTgim6+empOYiU5LS+B7gsd8U8/PpzJR61Ogpba0hLOHolTPNh0p32jSWS1EaaMTlblw7xeadN/HOV2/74UVmGL1jsznFzZvHWNrwBl8ilcIHt5kNiPivM+lIl76giUKPNhgnJ7DtKfpRxNLMGWhztGCzOULTDTxax4cZeiQdiAiW5sf+zjHMOtsgKT0EQmmBFIyD8HTSOFP5FzWwWPR/ZaIEyJAFI0mfNIWoqaoom04mAxNL6EXwp0XkYGIX1v+VU/gQV03bmlpx2lur2mHMY7V93ir55fWz89gLopFIaCBTqojH6evlL7xewZyuzQYLYeFZEwvoYpEcqKhkaRd/57YEszxikaSFA2F4HyIl7AyDPI5ZHxH4TUkdu5bTCRe303F0aTUDzIuneKIta0qd2PfTowpEjZT4Ed0S9SwjyJTMHLFIK6bNDkCTR1nbgi4DvV15SIJ01YFlGdjolSdx6FGQkmSVT8V4409ho2rkZgsWLMuCo9MjrxUFvG7VBJtlwdFmA9UjmDRIlvZFb5y2eBgsXlAxQbgNDD91PIuBEy2FLyEdHqhxmsGiH5IvA9t8eMx22MB+P9BHx9HmCI42nPhH2DImLUNrM8RmjNLzf52WhHtBTUkTBW6fuzwWTIWfGZvmuIMqMAvaeAJ4OjtDe2xbYmJZiQJYVt6MlDC+V0xqKy4uhTFsDwibhw10xFENL7i+RA+h0oqEgznxyXPHUAWlnjtTWnn8OP2sEJeAG5KMx3tS8s4ne6m7tLN3UcISq2+FDVVYW/J5gMMbEphMi99gCTt8XiM8hDYt9OQGCMlKYqBkNkRJVgM8+2bn9YHGZIoOjA4h0RUenlDAasiZMGES2mkA0gTSopW1IfooxXoHpNXWvBtCMMIiDVgWLtT82kkzVq8g2sh4BrXgmX9nhYx/k5PJ9VqWBc1mWljHri2Fv07PZ6x1wKxRCoaXd4mEFO5P7LUcaq5psLam5Jkp66IWjlxsTHsEhccEomsDk+drq1ZrC1AZlXaWyPMm33hXebzw+hJnpUTCchjnakuyQyDGbNGRdBxwx0Mv9bx0QgcFahASRLLfLWOGEpIwzmx2vK4K4Zms2OOwxf6iHEzALyEzhmiJvqUaXdghfnZm2qZDgAEvNlPD6hQpSshhyLgbHVPcGRHoMGyWhv3S0Hc7wMKFHo4si81hLicdLRJrwPM4mamULSyjEmbPxPWgAxOLHFi2U3HHEASR0ohiCiZSiBPgLN19kiWc0uSYiSdeOns5s7lz1QuloDVENk8SbHloM0Wvbo8+QNTafF6F7hNgC+sNNBWC0xUw95QqwR1jHgagoYEHR2lEkF0QEx3YVPFUdB5nuGY6YsSJzcMw7MhR1yywSB6adaNAJMe0F3PnK49j4FI2uuCHPtOM19tVMEeTkkuS8KIKM13yuZlCLx5rVCJXdtogg7fj6LE00pgwIOW0AHNDNUNdDm0mArTwplp8I0M5PlwN5kJr6RH2igMy7xTDzSrj0jzOf5YMsbQGsQbswxllFGID3hazXPDZQT4gSAY7JNY21pFRNz/zs2KiFtI+W6qQp6I41ChFU5uVgJn784xwetT9E5HmXpZ9nx0Vye+EmzPHmWvGchpy/xKZrmhJI9VmDUMlulMGXZWYd6Eqs9C3fK7/7evhmWACYEmNaxBgDOyNMfAI+WHaD9DYIJ2NBAdswVOOHVv9jNhJf5/lQnF55mMqnne9lDmv3bjug1M6gRi/muBDrC0DzTNMygAxgsCzNxC1AnKhWdmmIugrNHLYsc8XoAmiQXKcmSg5EBgEPRggW3qaZUaSkh4IfcNbOVecuL3L5LxWazhG2Eq+BhQwrjC82djYC8Zxg41tSEpD7wPSFixHx2jtGNquoWNJCyFafJWeCOeITl3SO/ZYxjHGZsHRfoH1LcwAtWgy1haobOIg3cgtVddeBoW26wXpgierOyEyxEI5tTY1ZMVzvhTl6U6D0cz7vorCxogcY3cqrg6F4LQEMDSIeUM5ZTkiC38j3W4qHgXhp2cHTY4+GXF//mYSSAjKrNCc04k7HmlA9ejomCGlvNcHK8Ow7rkLeFfJPkH1kYI2kQTXM+/B9ITnX7L2xL6+Xl+vr9+W68WuotfX6+v19f/1es2cr6/X12/p9Zo5X1+vr9/S6zVzvr5eX7+l12vmfH29vn5Lr9fM+fp6ff2WXv8XbdWvOr4kkc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  一个 穿着 黑色 衣服 的 男人 在 运动场 上 打篮球\n"
     ]
    }
   ],
   "source": [
    "path = './a02.jpg'\n",
    "predict_imgs(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d48a979b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8ea25e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0e60d1e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
